{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from pytorch_tabular.utils import make_mixed_dataset\n",
    "\n",
    "# %load_ext autoreload\n",
    "# %autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "data, cat_col_names, num_col_names = make_mixed_dataset(\n",
    "    task=\"classification\", n_samples=10000, n_features=20, n_categories=4\n",
    ")\n",
    "train, test = train_test_split(data, random_state=42)\n",
    "train, val = train_test_split(train, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "# Importing the Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "from pytorch_tabular import TabularModel\n",
    "from pytorch_tabular.models import GANDALFConfig\n",
    "from pytorch_tabular.config import DataConfig, OptimizerConfig, TrainerConfig\n",
    "from pytorch_tabular.models.common.heads import LinearHeadConfig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "## Define the Configs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "data_config = DataConfig(\n",
    "    target=[\n",
    "        \"target\"\n",
    "    ],  # target should always be a list. Multi-targets are only supported for regression. Multi-Task Classification is not implemented\n",
    "    continuous_cols=num_col_names,\n",
    "    categorical_cols=cat_col_names,\n",
    ")\n",
    "trainer_config = TrainerConfig(\n",
    "    auto_lr_find=True,  # Runs the LRFinder to automatically derive a learning rate\n",
    "    batch_size=1024,\n",
    "    max_epochs=100,\n",
    "    accelerator=\"auto\",  # can be 'cpu','gpu', 'tpu', or 'ipu'\n",
    ")\n",
    "optimizer_config = OptimizerConfig()\n",
    "\n",
    "head_config = LinearHeadConfig(\n",
    "    layers=\"\",  # No additional layer in head, just a mapping layer to output_dim\n",
    "    dropout=0.1,\n",
    "    initialization=\"kaiming\",\n",
    ").__dict__  # Convert to dict to pass to the model config (OmegaConf doesn't accept objects)\n",
    "\n",
    "model_config = GANDALFConfig(\n",
    "    task=\"classification\",\n",
    "    gflu_stages=3,  # Number of stages in the GFLU block\n",
    "    gflu_dropout=0.0,  # Dropout in each of the GFLU block\n",
    "    gflu_feature_init_sparsity=0.1,  # Sparsity of the initial feature selection\n",
    "    head=\"LinearHead\",  # Linear Head\n",
    "    head_config=head_config,  # Linear Head Config\n",
    "    learning_rate=1e-3,\n",
    ")\n",
    "\n",
    "\n",
    "tabular_model = TabularModel(\n",
    "    data_config=data_config,\n",
    "    model_config=model_config,\n",
    "    optimizer_config=optimizer_config,\n",
    "    trainer_config=trainer_config,\n",
    "    verbose=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "## Training the Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "Collapsed": "false",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Seed set to 42\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "You are using a CUDA device ('NVIDIA GeForce RTX 3060 Laptop GPU') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
      "/home/manujosephv/miniconda3/envs/lightning_upgrade/lib/python3.11/site-packages/pytorch_lightning/callbacks/model_checkpoint.py:639: Checkpoint directory saved_models exists and is not empty.\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "/home/manujosephv/miniconda3/envs/lightning_upgrade/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=19` in the `DataLoader` to improve performance.\n",
      "/home/manujosephv/miniconda3/envs/lightning_upgrade/lib/python3.11/site-packages/pytorch_lightning/loops/fit_loop.py:293: The number of training batches (6) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n",
      "/home/manujosephv/miniconda3/envs/lightning_upgrade/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=19` in the `DataLoader` to improve performance.\n"
     ]
    },
    {
     "data": {
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       "model_id": "58aa1a6aafc14219bcfe36c78602e49b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Finding best initial lr:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_steps=100` reached.\n",
      "Learning rate set to 0.09120108393559097\n",
      "Restoring states from the checkpoint path at /home/manujosephv/pytorch_tabular/docs/tutorials/.lr_find_fd8f0e95-a529-4577-9cd3-e484ff32cbef.ckpt\n",
      "Restored all states from the checkpoint at /home/manujosephv/pytorch_tabular/docs/tutorials/.lr_find_fd8f0e95-a529-4577-9cd3-e484ff32cbef.ckpt\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "┏━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
      "┃\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mName            \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mType            \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mParams\u001b[0m\u001b[1;35m \u001b[0m┃\n",
      "┡━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
      "│\u001b[2m \u001b[0m\u001b[2m0\u001b[0m\u001b[2m \u001b[0m│ _backbone        │ GANDALFBackbone  │ 14.5 K │\n",
      "│\u001b[2m \u001b[0m\u001b[2m1\u001b[0m\u001b[2m \u001b[0m│ _embedding_layer │ Embedding1dLayer │     92 │\n",
      "│\u001b[2m \u001b[0m\u001b[2m2\u001b[0m\u001b[2m \u001b[0m│ _head            │ Sequential       │     60 │\n",
      "│\u001b[2m \u001b[0m\u001b[2m3\u001b[0m\u001b[2m \u001b[0m│ loss             │ CrossEntropyLoss │      0 │\n",
      "└───┴──────────────────┴──────────────────┴────────┘\n",
      "\u001b[1mTrainable params\u001b[0m: 14.6 K                                                        \n",
      "\u001b[1mNon-trainable params\u001b[0m: 0                                                         \n",
      "\u001b[1mTotal params\u001b[0m: 14.6 K                                                            \n",
      "\u001b[1mTotal estimated model params size (MB)\u001b[0m: 0                                       \n"
     ]
    },
    {
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       "model_id": "c1d58011de6c49158458c34f2b601f52",
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      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<pytorch_lightning.trainer.trainer.Trainer at 0x7fdde261ea50>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tabular_model.fit(train=train, validation=val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "## Evaluating the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
     ]
    },
    {
     "data": {
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       "model_id": "789073550eef4c079b6155a7394e5ae1",
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      "text/plain": [
       "Output()"
      ]
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\">        Test metric        </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">       test_accuracy       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.9196000099182129     </span>│\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">         test_loss         </span>│<span style=\"color: #800080; text-decoration-color: #800080\">     0.203481063246727     </span>│\n",
       "└───────────────────────────┴───────────────────────────┘\n",
       "</pre>\n"
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       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1m       Test metric       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│\u001b[36m \u001b[0m\u001b[36m      test_accuracy      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.9196000099182129    \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[36m \u001b[0m\u001b[36m        test_loss        \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m    0.203481063246727    \u001b[0m\u001b[35m \u001b[0m│\n",
       "└───────────────────────────┴───────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/manujosephv/miniconda3/envs/lightning_upgrade/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:441: The 'test_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=19` in the `DataLoader` to improve performance.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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     },
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     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
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      ],
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       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = tabular_model.evaluate(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Native Global Feature Importance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some models like GANDALF, GATE, and FTTransformer have native feature importance, similar to the feature importance you get with GDBTs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cat_col_5</td>\n",
       "      <td>5.482856e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cat_col_19</td>\n",
       "      <td>3.333333e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>cat_col_9</td>\n",
       "      <td>1.183810e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>num_col_8</td>\n",
       "      <td>1.562194e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>num_col_7</td>\n",
       "      <td>1.480499e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>num_col_12</td>\n",
       "      <td>1.365922e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>num_col_18</td>\n",
       "      <td>1.304813e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>num_col_11</td>\n",
       "      <td>1.212310e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cat_col_10</td>\n",
       "      <td>1.032072e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>num_col_6</td>\n",
       "      <td>1.018892e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>num_col_16</td>\n",
       "      <td>1.012315e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>num_col_13</td>\n",
       "      <td>1.005833e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>num_col_1</td>\n",
       "      <td>9.984563e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>num_col_17</td>\n",
       "      <td>9.899406e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>num_col_4</td>\n",
       "      <td>9.853183e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>num_col_3</td>\n",
       "      <td>9.719336e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>num_col_14</td>\n",
       "      <td>9.535079e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>num_col_0</td>\n",
       "      <td>8.864745e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>num_col_15</td>\n",
       "      <td>8.863941e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>num_col_2</td>\n",
       "      <td>8.387005e-09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Features    importance\n",
       "0    cat_col_5  5.482856e-01\n",
       "3   cat_col_19  3.333333e-01\n",
       "1    cat_col_9  1.183810e-01\n",
       "11   num_col_8  1.562194e-08\n",
       "10   num_col_7  1.480499e-08\n",
       "13  num_col_12  1.365922e-08\n",
       "19  num_col_18  1.304813e-08\n",
       "12  num_col_11  1.212310e-08\n",
       "2   cat_col_10  1.032072e-08\n",
       "9    num_col_6  1.018892e-08\n",
       "17  num_col_16  1.012315e-08\n",
       "14  num_col_13  1.005833e-08\n",
       "5    num_col_1  9.984563e-09\n",
       "18  num_col_17  9.899406e-09\n",
       "8    num_col_4  9.853183e-09\n",
       "7    num_col_3  9.719336e-09\n",
       "15  num_col_14  9.535079e-09\n",
       "4    num_col_0  8.864745e-09\n",
       "16  num_col_15  8.863941e-09\n",
       "6    num_col_2  8.387005e-09"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tabular_model.feature_importance().sort_values(\"importance\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar plot, top 10 features\n",
    "tabular_model.feature_importance().sort_values(\"importance\", ascending=False).head(\n",
    "    10\n",
    ").plot.bar(logy=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Local Feature Attributions\n",
    "\n",
    "We can also use techniques like SHAP to get local feature attributions. This is a very powerful technique to explain the predictions of the model. We can use the `explain` method to get the local feature attributions for a given input.\n",
    "\n",
    "`PyTorch Tabular` supports these methods from `captum` for all models except Tabnet, TabTransformer, and MDN:\n",
    "\n",
    "\n",
    "- GradientShap: https://captum.ai/api/gradient_shap.html\n",
    "- IntegratedGradients: https://captum.ai/api/integrated_gradients.html\n",
    "- DeepLift: https://captum.ai/api/deep_lift.html\n",
    "- DeepLiftShap: https://captum.ai/api/deep_lift_shap.html\n",
    "- InputXGradient: https://captum.ai/api/input_x_gradient.html\n",
    "- FeaturePermutation: https://captum.ai/api/feature_permutation.html\n",
    "- FeatureAblation: https://captum.ai/api/feature_ablation.html\n",
    "- KernelShap: https://captum.ai/api/kernel_shap.html\n",
    "\n",
    "`PyTorch Tabular` also supports explaining single instances as well as batches of instances. But, larger datasets will take longer to explain. An exception is the `FeaturePermutation` and `FeatureAblation` methods, which is only meaningful for large batches of instances.\n",
    "\n",
    "Most of these explainability methods require a baseline. This is used to compare the attributions of the input with the attributions of the baseline. The baseline can be a scalar value, a tensor of the same shape as the input, or a special string like \"b|100\" which means 100 samples from the training data. If the baseline is not provided, the default baseline (zero) is used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Single Instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp = tabular_model.explain(test.head(1), method=\"GradientShap\", baselines=\"b|10000\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp = exp.T.sort_values(0, ascending=False)\n",
    "exp.columns = [\"GradientSHAP\"]\n",
    "exp.index.name = \"Features\"\n",
    "exp.reset_index(inplace=True)\n",
    "exp[\"colors\"] = \"red\"\n",
    "exp.loc[exp[\"GradientSHAP\"] > 0, \"colors\"] = \"green\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1120x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw plot\n",
    "plt.figure(figsize=(14, 10), dpi=80)\n",
    "\n",
    "# Plotting the horizontal lines\n",
    "plt.hlines(\n",
    "    y=exp.index,\n",
    "    linewidth=5,\n",
    "    xmin=0,\n",
    "    xmax=exp.GradientSHAP,\n",
    "    colors=exp.colors.values,\n",
    "    alpha=0.5,\n",
    ")\n",
    "# Decorations\n",
    "# Setting the labels of x-axis and y-axis\n",
    "plt.gca().set(ylabel=\"Features\", xlabel=\"GradientSHAP\")\n",
    "\n",
    "# Setting Date to y-axis\n",
    "plt.yticks(exp.index, exp.Features, fontsize=12)\n",
    "\n",
    "# Title of Bar Chart\n",
    "plt.title(\"GradientSHAP Local Explanation\", fontdict={\"size\": 20})\n",
    "\n",
    "# Optional grid layout\n",
    "plt.grid(linestyle=\"--\", alpha=0.5)\n",
    "\n",
    "# Displaying the Diverging Bar Chart\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiple Instances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp = tabular_model.explain(\n",
    "    test[test.cat_col_19 == 3], method=\"GradientShap\", baselines=\"b|10000\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_col_0</th>\n",
       "      <th>num_col_1</th>\n",
       "      <th>num_col_2</th>\n",
       "      <th>num_col_3</th>\n",
       "      <th>num_col_4</th>\n",
       "      <th>num_col_6</th>\n",
       "      <th>num_col_7</th>\n",
       "      <th>num_col_8</th>\n",
       "      <th>num_col_11</th>\n",
       "      <th>num_col_12</th>\n",
       "      <th>num_col_13</th>\n",
       "      <th>num_col_14</th>\n",
       "      <th>num_col_15</th>\n",
       "      <th>num_col_16</th>\n",
       "      <th>num_col_17</th>\n",
       "      <th>num_col_18</th>\n",
       "      <th>cat_col_5</th>\n",
       "      <th>cat_col_9</th>\n",
       "      <th>cat_col_10</th>\n",
       "      <th>cat_col_19</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.724874</td>\n",
       "      <td>-1.447601</td>\n",
       "      <td>0.005033</td>\n",
       "      <td>-0.023054</td>\n",
       "      <td>0.019009</td>\n",
       "      <td>0.008514</td>\n",
       "      <td>-0.083980</td>\n",
       "      <td>0.004687</td>\n",
       "      <td>-0.007507</td>\n",
       "      <td>-0.126826</td>\n",
       "      <td>0.010685</td>\n",
       "      <td>-0.100001</td>\n",
       "      <td>-0.000743</td>\n",
       "      <td>0.006760</td>\n",
       "      <td>-0.023292</td>\n",
       "      <td>0.008852</td>\n",
       "      <td>0.243883</td>\n",
       "      <td>0.129083</td>\n",
       "      <td>0.101006</td>\n",
       "      <td>-0.103718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.033266</td>\n",
       "      <td>0.354682</td>\n",
       "      <td>-0.023931</td>\n",
       "      <td>-0.027005</td>\n",
       "      <td>0.010930</td>\n",
       "      <td>0.001140</td>\n",
       "      <td>0.056476</td>\n",
       "      <td>-0.021277</td>\n",
       "      <td>0.001867</td>\n",
       "      <td>0.622130</td>\n",
       "      <td>0.026182</td>\n",
       "      <td>0.008232</td>\n",
       "      <td>-0.061105</td>\n",
       "      <td>0.012860</td>\n",
       "      <td>0.041835</td>\n",
       "      <td>-0.006741</td>\n",
       "      <td>1.755563</td>\n",
       "      <td>0.008189</td>\n",
       "      <td>-0.069742</td>\n",
       "      <td>0.023084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.274286</td>\n",
       "      <td>0.525516</td>\n",
       "      <td>0.021727</td>\n",
       "      <td>-0.084166</td>\n",
       "      <td>0.020813</td>\n",
       "      <td>0.001058</td>\n",
       "      <td>-0.048389</td>\n",
       "      <td>0.031756</td>\n",
       "      <td>-0.002266</td>\n",
       "      <td>-0.270582</td>\n",
       "      <td>0.013404</td>\n",
       "      <td>-0.043228</td>\n",
       "      <td>0.048533</td>\n",
       "      <td>-0.013822</td>\n",
       "      <td>0.038167</td>\n",
       "      <td>0.021330</td>\n",
       "      <td>-0.239157</td>\n",
       "      <td>-0.150969</td>\n",
       "      <td>-0.063134</td>\n",
       "      <td>-0.168519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.016431</td>\n",
       "      <td>-0.107677</td>\n",
       "      <td>0.015629</td>\n",
       "      <td>0.012411</td>\n",
       "      <td>-0.006554</td>\n",
       "      <td>0.001735</td>\n",
       "      <td>0.009270</td>\n",
       "      <td>0.024726</td>\n",
       "      <td>-0.001018</td>\n",
       "      <td>0.586440</td>\n",
       "      <td>0.003225</td>\n",
       "      <td>-0.019198</td>\n",
       "      <td>0.015317</td>\n",
       "      <td>-0.002911</td>\n",
       "      <td>0.001742</td>\n",
       "      <td>-0.002891</td>\n",
       "      <td>0.287464</td>\n",
       "      <td>0.171883</td>\n",
       "      <td>-0.011178</td>\n",
       "      <td>-0.051022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.004894</td>\n",
       "      <td>1.684316</td>\n",
       "      <td>0.016587</td>\n",
       "      <td>-0.357520</td>\n",
       "      <td>0.022842</td>\n",
       "      <td>-0.013848</td>\n",
       "      <td>0.005630</td>\n",
       "      <td>0.025678</td>\n",
       "      <td>-0.004070</td>\n",
       "      <td>0.802940</td>\n",
       "      <td>-0.027120</td>\n",
       "      <td>0.062992</td>\n",
       "      <td>0.179163</td>\n",
       "      <td>0.001076</td>\n",
       "      <td>0.019801</td>\n",
       "      <td>-0.022297</td>\n",
       "      <td>-1.746769</td>\n",
       "      <td>0.793595</td>\n",
       "      <td>-0.042146</td>\n",
       "      <td>0.055857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num_col_0  num_col_1  num_col_2  ...  cat_col_9  cat_col_10  cat_col_19\n",
       "0  -1.724874  -1.447601   0.005033  ...   0.129083    0.101006   -0.103718\n",
       "1   0.033266   0.354682  -0.023931  ...   0.008189   -0.069742    0.023084\n",
       "2   0.274286   0.525516   0.021727  ...  -0.150969   -0.063134   -0.168519\n",
       "3   0.016431  -0.107677   0.015629  ...   0.171883   -0.011178   -0.051022\n",
       "4  -0.004894   1.684316   0.016587  ...   0.793595   -0.042146    0.055857\n",
       "\n",
       "[5 rows x 20 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp_agg = exp.abs().sum().to_frame()\n",
    "exp_agg = exp_agg.sort_values(0, ascending=True)\n",
    "exp_agg.columns = [\"GradientSHAP\"]\n",
    "exp_agg.index.name = \"Features\"\n",
    "exp_agg.reset_index(inplace=True)\n",
    "exp_agg[\"colors\"] = \"red\"\n",
    "exp_agg.loc[exp_agg[\"GradientSHAP\"] > 0, \"colors\"] = \"green\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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e3L/+9S/s37/f5bakpCQsX768x7EdWbrzzjs7PZT9tttug9FoRF1dHfbv34/Ro0e73N/VazxkyBD5kndff/01pk2b5nJ/fn4+PvroI+Tn58NqtcrngXecm3/06FEMGjSox/pFdXd5r47TFH5r2bJl+Pe//41vv/0Ww4cPR0NDA+bPn9/j9nz33XefcZtarcYdd9yB7Oxs5ObmnvF6dMXhcOCTTz7Bvn37UFNTA4fDAUmSYLVaAaDLc9qVSiWuv/76M26Pjo6W38+TJ08iOjra5f6TJ09ix44dOHToEOrq6uTL2h07dqzb9fVWW1sbvv32WwDo8vJ+f/rTn/Dhhx92OdFkd79zPvroo7P6nUNE5w822kREPqhjdtzOzi8FgJtuusnl3Mhrrrmm2/OIu7usUE1NDSZPnozdu3d3W5PFYpEb7ePHjwMAUlJSOn1sx2REBw8e7HaZHYqKiiBJEmw2W4/nDXf1mnTVrOh0OgCnLm11ti6//HJ5sjlJkvDDDz/gueeew5YtW/DZZ59h5cqVePTRR88Yd+utt3Y5ARNw6g/8rhrtV155RV7GbyfDmzRpEsLDw1FSUoLPP/8c1157bafLOP062iqVCpGRkfjd736H66+/Hkql2J8Ap8/UfOLECXkitdOdPmnU7t278fvf/15o2QDkZqS7LKWkpKCurq7TxqWrcR33HThwQM4rcGoysQceeADr1q3r9vzijvPY+8rZXN4rKCgIW7ZsQf/+/dHQ0IBhw4b1+OWFwWCAwWDo9L6O1+r016M7e/fuxR133IHS0tIuH9PV6xQTE4OgoKBO79PpdKirqztjm9ywYQP+/Oc/o6mpqdfr662TJ0/K6+8qQx3zOHTVMHvydw4R+T822kREPigjIwObNm3CgQMH0N7e3uUeS1EajabL+7KysrB7925ceeWV+J//+R8MGzYMRqNR/iM5NjYWlZWVHp1Ft2P2Yq1Wi8mTJ5/VMtx9jUQpFApkZGRg8+bNaG5uxvbt2/H+++932mifrZaWFrzxxhsATk0U1tlRCh17YF955ZUuG+3fXkf7bCQnJ8NkMsFsNuP777/vtBZfd3p2V65cibVr1yI6OhrZ2dkYNWoUoqKi5Mu3TZs2DZs3b/aZWaO3bNki79UtLS1FZWUlkpKS3FqmyHNrbm7GrbfeiurqasyaNQtz5sxBeno6dDodAgMD8euvv2LAgAFdLqu32+P+/ftx3333ITAwEMuWLcPNN9+MxMREhISEQKFQYP369bjvvvt85n0Bzt3vHCLyT2y0iYh80E033YRHHnkEdXV12LlzZ5/MPN6ZpqYm7Ny5EwEBAdi5c+cZe8KamppQVVV1xri4uDgA3V8eqqSkRLiOhIQEAKea2FdffdVv/oC97rrrsH37dtTW1vbpcj/44AN5mYWFhSgsLOzyse+//z7MZjNMJlOf1tAhICAAN954IzZt2oQ33ngDDz/8cJ8uvyNL3T3HoqIil8d2dl9nOvIZHx8v37Z161YAwLp16zBp0qQzxnQcouwLdu/ejSVLliAkJASTJk3Cli1bcMcdd+Cbb77pcm9xfX096uvrO92r3dnr0ZWvv/4a1dXVyMjI6HTm7b5+nd555x1IkoT58+dj0aJFHl9fv379oFar4XA4UFhYiKFDh57xmI5MdpY7IqKe+MdfMkREF5j09HTccccdAIAFCxYIn+vcWw0NDXA6ndDpdJ3+Yf7GG290ugdp7NixAICPPvoIZrP5jPu3b9/e7fW3fys2NhZDhw6F1WrFRx99JDzOHR3nVLe1tXV6v8ies45DakUal97ouHb2Y489BunUxKWd/rv88svhcDjkvd+e8vjjj0OpVGL//v146aWX+nTZHefev/32250eapuTk4O6ujqEhYVhxIgRZ9z/448/4scffzzj9sOHD+PAgQMICAjAVVddJd/ekdfO9gofPnwYeXl5Z/lM+lZtbS3uvPNOtLW1YfXq1di0aROuvPJKfPfdd3jssce6Hbtp06YzbmtpacHbb78N4P+95t3peJ26Ojy6rzPX3ftit9vx7rvvdjqup+24K0qlUj46Y+PGjZ0+puMLho5rdRMR9QYbbSIiH7VmzRqkp6fj2LFjGDVqFL766qtOH1dcXCx8zuVvRUVFwWg0or6+/ow/zvfu3Ysnnnii03G///3vkZGRgcbGRsybNw8Oh0O+r6ysDAsXLux1LU8//TSAUxN4dVy3+XSSJOG7777DJ5980utld6ajOT58+HCn97/00ku4++67O518TZIkvPfee1i9ejWAUxN59ZXS0lJ89tlnADqf1Op0d911FwD3rqktYtCgQVixYgUA4IEHHsDixYs7/SLF6XRi7969vVr2H//4RyQmJqKiogILFixwaZiKiorwyCOPAADmz58vH959OkmSMGfOHNTV1cm3NTQ0YM6cOZAkCZMnT5aPmABOTVQFnNq+Ok5ZAIDKykrcddddvW7YPEGSJMyYMQPl5eW4++67MWvWLCiVSmzZsgUmkwkrVqzABx980OX4p556CocOHZJ/bm9vx2OPPYbjx48jISFB6PSMjtfp888/x88//+xy3/r16+Wmva90rO+1116TJ1oDTjXZc+fO7fLIhY7t+NixY/Ih9qI6svXyyy+fMcfFxo0bsX37dgQFBeGhhx7q1XKJiAAeOk5E5LOMRiO+/fZbTJs2DZ9//jkyMzMRHx+PSy+9FAaDATabDceOHcNPP/0ESZIwZMgQjBw5slfrCAwMxF//+lf8+c9/xl133YU1a9YgNTUVpaWl2LNnD2bMmIGvv/6608PAN23ahMzMTGzZsgVff/01xowZg+bmZnzxxRcYOnQowsPD8e9//1u4lptvvhkrV67EI488gkmTJiE9PR0DBgyAXq/HiRMncPDgQdTU1OCxxx7Ddddd16vn2Zlbb70V//M//4MXX3wRhw4dQkJCAgICAjBp0iRMmjQJra2teP311/H6668jIiICw4cPR3h4OOrr6/Hzzz/Lh+HOmDEDf/rTn9yup8M///lPtLe347LLLpObj67ceeedWLBgAQ4ePIj9+/d3use3rzzwwAMICwvDAw88gGeffRbPP/88RowYgfj4eAQFBeHEiRPYv38/zGYzVCrVGTOld0WtVmPbtm24/vrr8fLLL2Pnzp343e9+B6vVii+++AJ2ux0TJkzAk08+2en4SZMm4dChQ0hNTcW4ceOgUCiQm5sLs9mM/v37y1+GdFi8eDE++ugjbNiwAV9++SUyMjJgsVjw1VdfITU1FX/4wx+Qk5Pj9uv1WwsXLuxy1nEAePDBB5GRkQEAeOaZZ/Dxxx9j0KBBLkcQJCYmYuPGjbjlllswa9YsHDhw4IwJ1hITEzFixAhkZGQgMzMT/fr1w759+1BQUIDQ0FC89dZbnX5h8VvDhw/HLbfcgg8++ADDhw9HZmYmTCYT8vLycPToUSxevBh/+9vfzu7F6MSsWbOwcuVK/PDDD0hJScHvf/97BAYG4ptvvoHNZsNDDz2ElStXnjEuMTERI0eOxPfffy//DgwODkZ4eDj+/ve/d7vOG264AUuWLMHTTz+Na6+9FqNHj0ZiYiKOHDmCAwcOIDAwEGvXrsXgwYP77HkS0QXk3FxFjIiI3PHZZ59Js2fPlgYMGCDpdDpJqVRKRqNRysjIkO677z7p008/lZxO5xnjOq73+ttrWf/W+++/L40aNUoyGAySVquVRo4cKb300ktSe3t7t9fYLSkpkWbOnClFRUVJKpVKSk1NlR577DGpqampy3X3dP3qn376Sbr33nul/v37S8HBwVJISIiUmpoqTZgwQXrxxRel8vJyl8f3dA3g7taXk5MjjR49WgoLC5MUCoXLtXYtFov0/vvvS/Pnz5cuv/xyKT4+XgoKCpI0Go2UlpYmTZ06Vdq1a1en6+x47j1dc7mjto7raJ/+eq9evbrbsR1uvfVWCYA0Z84c+bbOrqPdV06ePCktW7ZMGj9+vBQdHS2pVCpJo9FICQkJ0sSJE6Xs7GypoqLijHFdXUe7Q2lpqTRv3jwpNTVVUqlUUlhYmHTllVdKL7/8stTa2nrG4zuuo3333XdLNTU10n333SfFx8dLKpVKSkhIkB588EHp5MmTna7rxx9/lCZNmiTFxMRIwcHBUv/+/aVFixZJFouly7x48jraAKScnBxJkiQpNzdXCgwMlEJCQs64lneHRx55RAIgXX755fK1qE9/fVtbW6W//e1v0sCBAyW1Wi2ZTCZp8uTJXS6vq22opaVFeu6556QhQ4ZIISEhkslkkq677jrpk08+6fL97Ol97m59J06ckObOnSulpaVJarVaio2NlWbMmCEdO3bM5f3+rZKSEmnatGlSTEyMpFQqz1h/T+/drl27pIkTJ0r9+vWTlEqlFB0dLf3xj3+Uvvvuu04f39Pv1bPJChGdfxSS5EPTNxIRERFRrxUXFyMlJQVJSUndTlJIRETnBs/RJiIiIiIiIupDbLSJiIiIiIiI+hAbbSIiIiIiIqI+xHO0iYiIiIiIiPoQ92gTERERERER9SFeR9tPqdVqREREeLuMLkmSBIVC4e0yyMcxJySKWSERzAmJYlZIBHNC3Tlx4gQcDkeX97PR9lMRERE4fvy4t8voUn5+PtLT071dBvk45oREMSskgjkhUcwKiWBOqDvx8fHd3s9Dx4mIiIiIiIj6EBttIiIiIiIioj7EWcf9VHx8vE8fOu50OhEYGOjtMsjHMSckilkhEcwJiWJWSARzQt3pqR/jHm3yiPr6em+XQH6AOSFRzAqJYE5IFLNCIpgTcgcbbfKIuro6b5dAfoA5IVHMColgTkgUs0IimBNyBxttIiIiIiIioj7ERps8Ijg42NslkB9gTkgUs0IimBMSxayQCOaE3MHJ0PyUr0+GRkREREREdL7iZGjkFWVlZd4ugfwAc0KimBUSwZyQKGaFRDAn5A422uQRDofD2yWQH2BOSBSzQiKYExLFrJAI5oTcwUabiIiIiIiIqA+x0SaPMJlM3i6B/ABzQqKYFRLBnJAoZoVEMCfkDjba5BE6nc7bJZAfYE5IFLNCIpgTEsWskAjmhNzBRps8ori42NslkB9gTkgUs0IimBMSxayQCOaE3MFGm4iIiIiIiKgPsdEmIiIiIiIi6kNstMkjEhISvF0C+QHmhEQxKySCOSFRzAqJYE7IHWy0ySN43UESwZyQKGaFRDAnJIpZIRHMCbmDjTZ5RE1NjbdLID/AnJAoZoVEMCckilkhEcwJuYONNhEREREREVEfYqNNHqFUKr1dAvkB5oREMSskgjkhUcwKiWBOyB0KSZIkbxdBvRcfH4/jx497uwwiIiIiIqILTk/9GPdok0dUVlZ6uwTyA8wJiWJWSARzQqKYFRLBnJA7eDxEL+Tm5mLcuHFobW3loSQ9+OfBfyKyMtLbZZCPq6mpYU5ICLNCIpgTEsWskAjmxPuyMrK8XcJZY7foIVu2bMGaNWtw8OBBWK3WM5rz7777Dk8//TT27duH5uZmJCUlYcGCBZg1a5YXq+47Vc1VaLG0eLsM8nHmZjNzQkKYFRLBnJAoZoVEMCfkDjbaHmI0GjF37lzYbDb86U9/OuP+kydPYvLkyXjllVcQERGB3Nxc3HLLLTAajbj11lvPfcF9TK1We7sE8gPMCYliVkgEc0KimBUSwZyQOzzeaGdmZmLYsGE4ceIEduzYAZ1Oh8WLF2POnDnYuHEjlixZ4nIS+dKlS/HZZ59h9+7d8vghQ4agpqYGu3btgk6nw/LlyzF48GDce++9OHToEC6++GJs2rQJAwYM6LEes9mMJUuWYNeuXaitrUVcXBxWrlyJCRMmwOl0Ijs7Gxs2bEB1dTX69++Pp556CjfccEOvn/eECRMAnDrcvDMTJ050+XncuHEYP348vvzyy04b7ezsbGRnZ8s/W61W5OfnAwCCg4Plk/HtdjsAwGQyQa/Xo6ioSB4THx+PlpYW+ZqAgYGBSElJQVVVFRobGwEAYWFhiIqKQmFhIdrb2wEAUVFRUCqVKC8vl5eVkpKC+vp61NXVudRQVlYGh8MBh8OBgIAAqNVq1NfXy+N0Oh2cTieampoAAAEBATAYDPJef+DUL7XQ0FCYzWZ5nFarhUKhgNVqlW8zGo2w2Wzyc1YqldDpdGhoaIDT6QQAaDQaqFQqNDQ0uNTQ1taG5ubmPqmhubkZDocDABAUFISwsDDU19fLr19ISAiCgoJcatDr9Whtbe2xhpCQEPk17nh/JEmS36+O97qpqanHGpRKJSwWi0sNLS0tsNlsch70ej0sFgva2trk91Wj0fRJDaGhoQgMDHSpwWAwwGaz9VhDcHCwS47CwsLQ3t4u50ihUMBoNKKxsREtLae+eVapVNBqtairq0PHnI9d1eBwOOQaOnJ0eg0ajabHLPemhoCAAJccGQwG2O32HrN8oW9PDoeD21MP29PpWb5QtyeNRsPtCfx86qmG1tZW+TXk9sTPp+5qcDgc3J56UUNfb09WqxXV1dXy65eamorq6mr5/dFqtYiOjkZRUZGch8jISKhUKpceMyUlBQ0NDfJ73Rf9U088Put4ZmYmfvjhB+Tk5CAzMxM5OTmYMmUKjh49it27dws12nl5edi+fTvGjBmDVatWYfHixcjMzMSaNWsQGxuLqVOnorGxER9//HG3tUiShLFjx0Kr1WLt2rVISEhAUVER7HY7Bg0ahOeffx4rVqzA9u3bMXToUGzbtg133XUX9u7di4yMjLM6R1t0jMViwYABA/Dss89i5syZPS7X12cdf/C9B2EymbxdBvk4s9nMnJAQZoVEMCckilkhEcyJ9y3NXOrtErrUUz92Tg4dnzx5MsaPHy//32QyYf/+/b0af9VVVwEAZs6ciYcffhgzZsxAcnIyAGDatGnIyur5RPn9+/dj9+7dqK6uRkREBAAgNTVVvn/9+vV49NFHkZGRAQC48847sXnzZqxfvx5r164Vrre3WlpacMcdd2DgwIGYMWOGx9ZzLkWHRCNSx8kjqHsqu4o5ISHMColgTkgUs0IimBNyxzlptGNjY11+Dg0NhdVqFd4rHBMT4zK2s9tOP8SkK0VFRTAajXKT/VtlZWVIS0tzuS09PR2//PKLUJ1no7m5GbfddhtaWlrwr3/967yZzXxK+hSkp6d7uwzycfn5+cwJCWFWSARzQqKYFRLBnJA7vHod7bCwMPncjQ4VFRUeW19ycjLq6upQW1vb6f0JCQkoKChwua2goACJiYkeqaeurg7XXHMNlEoldu7cCa1W65H1eEN0dLS3SyA/wJyQKGaFRDAnJIpZIRHMCbnDq4328OHDYbVa8fbbb6O9vR25ubl45513PLa+kSNHYtSoUZg1a5Z8PH1RUZG8xzorKwvLly9HXl4e2trasHXrVuzcuVPosPTfcjqdsNvt8qQTDocDdrtdniigqqoKY8eORUJCAnJychAcHNxHz9I3BAYGersE8gPMCYliVkgEc0KimBUSwZyQO7zaaKempmL16tVYuHAhDAYD1q1b59HrSCsUCnzwwQeIiYnBlVdeibCwMEycOBFlZWUAgAULFmDevHm4/fbbYTKZsGzZMrz33nsYOXJkr9e1adMmaDQaefZxrVYLjUaDr7/+GgCwbt06/PTTT/jwww9hNBqh1Wqh1WrPaoZzX3T6DOVEXWFOSBSzQiKYExLFrJAI5oTc4fFZx8kzfH3WcZ7TQiKYExLFrJAI5oREMSskgjmh7vTUj3l1jzYRERERERHR+ea826Pd1YRiBoOhT/cADx48GCUlJZ3eV15eDr1e32fr6oyv79Fub29HQAC/x6HuMSckilkhEcwJiWJWSARzQt3xieton0uNjY3nZD2HDx8+J+vxV2azGeHh4d4ug3wcc0KimBUSwZyQKGaFRDAn5A5+RUMeUV9f7+0SyA8wJySKWSERzAmJYlZIBHNC7mCjTURERERERNSH2GiTR2g0Gm+XQH6AOSFRzAqJYE5IFLNCIpgTcsd5NxnahcLXJ0MjIiIiIiI6X/HyXuQVpaWl3i6B/ABzQqKYFRLBnJAoZoVEMCfkDjba5BEtLS3eLoH8AHNCopgVEsGckChmhUQwJ+QONtpEREREREREfYiNNnlEv379vF0C+QHmhEQxKySCOSFRzAqJYE7IHWy0ySO0Wq23SyA/wJyQKGaFRDAnJIpZIRHMCbmDjTZ5RElJibdLID/AnJAoZoVEMCckilkhEcwJuYONNhEREREREVEfYqNNRERERERE1IfYaJNHJCQkeLsE8gPMCYliVkgEc0KimBUSwZyQO9hok0fY7XZvl0B+gDkhUcwKiWBOSBSzQiKYE3IHG23yiBMnTni7BPIDzAmJYlZIBHNCopgVEsGckDuU3i7gfLNx40YsWbIEx48f93YpXrU1fysiLZHeLoN8XE1NDXNCQpgVEsGckChm5cKRlZHl7RLoAnXeN9rFxcVISUnBsWPHkJ6e7u1yXMycORNvvvkm1Gq1fNu8efOwbNkyL1bVN2rsNWixtHi7DPJx9fZ65oSEMCskgjkhUcwKiVAqz/tWiTyI6fGyO+64A2+88Ya3y+hzBoPB2yWQH2BOSBSzQiKYExLFrJCI5ORkb5dAfsxvGm2bzYa//e1v2Lp1KyorKxEREYEnn3wSI0aMwEMPPYQff/wRLS0tGDhwIJ599lmMHz8eADB48GAAwLBhw6BQKDBjxgysXbu223UdP34cjz32GL766itYLBakpqbi1VdfRUZGBux2O5588kls3boV9fX1uOSSS7B8+XJcccUVHn3+2dnZyM7Oln+2Wq3Iz88HAAQHByM+Ph7Hjx+XJ20wmUzQ6/UoKiqSx8THx6OlpQU1NTUAgMDAQKSkpKCqqgqNjY0AgLCwMERFRaGwsBDt7e0AgKioKCiVSpSXl8vLSklJQX19Perq6lxqKCsrg8PhQGVlJQwGA9RqNerr6+VxOp0OTqcTTU1NAICAgAAYDAZYrVa0trYCANRqNUJDQ2E2m+VxWq0WCoUCVqtVvs1oNMJms8nPWalUQqfToaGhAU6nEwCg0WigUqnQ0NDgUkNbWxuam5v7pIbm5mY4HA4AQFBQEMLCwlBfXy+/fiEhIQgKCnKpQa/Xo7W1tccaQkJC5Ne44/2RJEl+vzre66amph5rUCqVsFgsLjW0tLTAZrPJedDr9bBYLGhra5PfV41G0yc1hIaGIjAw0KUGpVKJoKCgHmsIDg52yVFYWBja29vlHCkUChiNRjQ2NqKl5dQeCpVKBa1Wi7q6OkiS1GUNBoMBDodDrqEjR6fXoNFoesxyb2oICAhwyZHBYIDdbu8xyxfy9mSz2eRtgtuTWJYvxO3J4XAgKiqK2xP4+dRTDVVVVVCpVAC4PZ3vn08dfy8rlUokJyejsrJSrkun0yEyMhIFBQXy6xATEwOFQoGKigpYLBbodDqkpaWhtrZWXn5ISAhiY2NRUlIiP5/w8HCEhISgtLRUriExMRHNzc2ora0FcCp/SUlJqKiokLcxvV6P8PBwFBQUyONiY2MhSRIqKyvl9zAtLQ01NTVyRkJDQxETE4Pi4mI5DxEREQgODkZZWZm8rKSkJDQ2NuLkyZPye5+YmIjy8nI5WwaDASaTCYWFhfK4uLg4OJ1OVFVV9aqGyMhIqNVqlxqSk5NhsVjk91qtViMhIcGldzEajTAYDC69S1xcHNra2lBdXS3nKDU1FdXV1XJGtFotoqOjUVRUJOchMjISKpXK5VTelJQUNDQ0yDX0Rf/UE4XUkSofN2PGDPz666947bXXMHDgQFRWVsq/JCsrKzFmzBgEBATg2WefxYoVK3Ds2DFERkb2+tBxm82GYcOGYfTo0XjuuedgMplw5MgRhIaGIikpCfPnz0dubi5ycnKQmJiIVatWYenSpfjll18QHx/fq3O0Z86ciffffx+BgYEwGAy49tpr8dRTTyEiIqLHsR3B8FUPvvcgTCaTt8sgH2c2m5kTEsKskAjmhEQxKxeOpZlLz3psfn6+z516Sr6jp37ML/Zo19bW4s0338S+fftw8cUXAzj1TU9sbCwA4JJLLpEfu3TpUrzwwgv47rvvcPPNN/d6XTt27MDJkyexdu1a+dzpQYMGAQDa29vxyiuvYPPmzfJG98gjj+D111/HG2+8gccff7xX65o/fz7+/ve/IyoqCgUFBZgzZw4mTZqEPXv2QKFQ9Lp2XxIdEo1IHScZoe6p7CrmhIQwKySCOSFRzAoReZpfNNodu+8HDBhwxn2lpaVYtGgR9uzZg/r6egQEBMBisci7989mXcnJyS4TlHWora2FzWZDWlqay+3p6ekuh4mIGjFihMsyNmzYIO99v+iii3pfvA/JyshCZCQ/wKh7NTU1zAkJYVZIBHNCopgVEqHT6bxdAvkxv7iOdsdEBL/++usZ991zzz1ob2/Hvn37YLFYUFdXB51OJ59nERDQu6eYnJyM4uJi+RyW04WHhyM4ONjlHAoAKCgoQGJiYq/W05mOWv3kaP5u8cOLRDAnJIpZIRHMCYliVkgEc0Lu8ItGOyIiAlOnTsW8efNw9OhRAEBlZSUOHDiAhoYGaLVaGI1GNDU14YknnnCZBCMiIgIBAQHyuJ7cdNNNMBqNmDt3LmprayFJEn7++WeUlJQgICAAs2fPxl//+lcUFhaipaUFK1asQH5+PqZPn96r52S327Ft2zZ5UoXi4mLce++9GDFiBPr379+rZfmi334ZQdQZ5oREMSskgjkhUcwKiWBOyB1+0WgDwIYNGzB27FjccMMN0Gq1GD16NA4fPowXX3wRBw8ehNFoxKBBgxAXF4f4+Hh5nEajwTPPPIOsrCwYDAbMnTu32/VoNBp88cUXaGxsxJAhQ6DX6zF9+nR5hrrly5fjuuuuw7hx4xAZGYl3330Xn376KRISEnr1fNrb27Fy5UqkpKQgNDQUY8eORVJSEj788MNe74X3RefDXnnyPOaERDErJII5IVHMColgTsgdfjPrOLny9VnHOUsjiWBOSBSzQiKYExLFrJAI5oS601M/5v+7TsknxcTEeLsE8gPMCYliVkgEc0KimBUSwZyQO/xi1vG+VFpaKl+u67eGDh2KPXv29Nm6tFptp7cbDAaf3hvdF/z98mR0bjAnJIpZIRHMCYliVkgEc0LuuOD2aCcmJqKxsbHTf33ZZAPocj3ne5MNABUVFd4ugfwAc0KimBUSwZyQKGaFRDAn5I4LrtEmIiIiIiIi8iQ22kRERERERER9iLOO+ylfn3VckiSe10I9Yk5IFLNCIpgTEsWskAjmhLrDWcfJK2pra71dAvkB5oREMSskgjkhUcwKiWBOyB1stMkjGhoavF0C+QHmhEQxKySCOSFRzAqJYE7IHWy0iYiIiIiIiPoQG23yiJCQEG+XQH6AOSFRzAqJYE5IFLNCIpgTcgcnQ/NTvj4ZGhERERER0fmKk6GRV5SUlHi7BPIDzAmJYlZIBHNCopgVEsGckDvYaJNHtLa2ersE8gPMCYliVkgEc0KimBUSwZyQO9hoExEREREREfUhNtrkEeHh4d4ugfwAc0KimBUSwZyQKGaFRDAn5A422uQRnKWRRDAnJIpZIRHMCYliVkgEc0LuYKNNHlFaWurtEsgPMCckilkhEcwJiWJWSARzQu5go01ERERERETUh9hoExEREREREfUhhSRJkreLOF/k5uZi3LhxaG1thVKp9Oi6erpAuret/c9aj78G5P/a2tqYExLCrJAI5oREnS9ZycrI8nYJ57WWlhaoVCpvl0E+qqd+zP9/w/ipLVu2YM2aNTh48CCsVus5ac7PpWJzMYKDg71dBvk4u93OnJAQZoVEMCckilkhEc3NzWy06azx0HEvMRqNmDt3Ll544QVvl+IRzc3N3i6B/ABzQqKYFRLBnJAoZoVE1NbWersE8mNe34WamZmJYcOG4cSJE9ixYwd0Oh0WL16MOXPmYOPGjViyZInLLvmlS5fis88+w+7du+XxQ4YMQU1NDXbt2gWdTofly5dj8ODBuPfee3Ho0CFcfPHF2LRpEwYMGNBjPWazGUuWLMGuXbtQW1uLuLg4rFy5EhMmTIDT6UR2djY2bNiA6upq9O/fH0899RRuuOGGXj/vCRMmADh1uLmI7OxsZGdnyz9brVbk5+cDAIKDg+VDF+x2OwDAZDJBr9ejqKhIHhMfH4+WlhbU1NQAAAIDA5GSkoKqqio0NjYCAMLCwhAVFYXCwkK0t7cDAKKioqBUKlFeXi4vKyUlBfX19airq3OpoaysDA6HA83NzdBoNFCr1aivr5fH6XQ6OJ1ONDU1AQACAgJgMBjkvfoAoFarERoaCrPZLI/TarVQKBSwWq3ybUajETabTX7OSqUSOp0ODQ0NcDqdAACNRgOVSoWGhgaXGtra2uQPWXdraG5uhsPhAAAEBQUhLCwM9fX18usXEhKCoKAglxr0ej1aW1t7rCEkJER+jTveH0mS5Per471uamrqsQalUgmLxeJSQ0tLC2w2m5wHvV4Pi8WCtrY2+X3VaDR9UkNoaCgCAwNdamhvb4fNZuuxhuDgYJcchYWFob29Xc6RQqGA0WhEY2MjWlpaAAAqlQparRZ1dXXoOEOmsxoMBgMcDodcQ0eOTq9BJMu9qSEgIMAlRwaDAXa7vccsX8jbU8fyuT11vT39NssX4vbU3NzM7em0Gvj51HUNNptNfg39eXsym81e/Xuv4zXW6XQoLi6WxyUkJMDhcMg1KJVKJCcno7KyUn5tdDodIiMjUVBQIL8O0dHRCAwMdKkhNTUVZrNZfp01Gg3i4uJQWloqv6b9+vWDVqtFSUmJSw12ux0nTpzoVQ0xMTFQKBSoqKiQG+20tDTU1tbK20pISAhiY2NRUlIibxfh4eEICQlxmak8MTERzc3N8nKCgoKQlJSEiooKeRvT6/UIDw9HQUGBPC42NhaSJKGyslLOUVpaGmpqauSMhIaGIiYmBsXFxXIeIiIiEBwcjLKyMnlZSUlJaGxsxMmTJwGcyl9iYiLKy8vlbBkMBphMJhQWFsrj4uLi4HQ6UVVV1asaIiMjoVarXWpITk6GxWKRtzm1Wo2EhASX3sVoNMJgMLhkOS4uDm1tbaiurgZw6vdRamoqqqur5d93Wq0W0dHRKCoqkn+/RkZGQqVSufSQKSkpaGhokGvoi/6pJ14/RzszMxM//PADcnJykJmZiZycHEyZMgVHjx7F7t27hRrtvLw8bN++HWPGjMGqVauwePFiZGZmYs2aNYiNjcXUqVPR2NiIjz/+uNtaJEnC2LFjodVqsXbtWiQkJKCoqAh2ux2DBg3C888/jxUrVmD79u0YOnQotm3bhrvuugt79+5FRkbGWZ2jfbbndfv6OdoPv/8wDAaDt8sgH1dfX8+ckBBmhUQwJyTqfMnK0syl3i7hvFZSUoKkpCRvl0E+yi/O0Z48eTLGjx8v/99kMmH//v29Gn/VVVcBAGbOnImHH34YM2bMQHJyMgBg2rRpyMrqebKI/fv3Y/fu3aiurkZERASAU9+idVi/fj0effRRZGRkAADuvPNObN68GevXr8fatWuF670QXJJ4ibdLID8Qr4v3dgnkJ5gVEsGckChmhUSwySZ3+ESjHRsb6/JzaGgorFar8B7emJgYl7Gd3Xb64VRdKSoqgtFolJvs3yorK0NaWprLbenp6fjll1+E6ryQTIyeeMb7SvRbFRUVzAkJYVZIBHNCopgVEsGckDt8ejK0sLAw+RyKDhUVFR5bX3JyMurq6rqc+CAhIcHl/AkAKCgoQGJiosdq8lecZIREMCckilkhEcwJiWJWSARzQu7w6UZ7+PDhsFqtePvtt9He3o7c3Fy88847HlvfyJEjMWrUKMyaNUs+3r6oqEjeY52VlYXly5cjLy8PbW1t2Lp1K3bu3Cl0WPpvOZ1O2O12eRIHh8MBu90uT0hBRERERERE/smnG+3U1FSsXr0aCxcuhMFgwLp16zBr1iyPrU+hUOCDDz5ATEwMrrzySoSFhWHixInyrHkLFizAvHnzcPvtt8NkMmHZsmV47733MHLkyF6va9OmTdBoNPLs41qtFhqNBl9//XWfPidv0ev13i6B/ABzQqKYFRLBnJAoZoVEMCfkDq/POk5nx9dnHZckCQqFwttlkI9jTkgUs0IimBMSxayQCOaEutNTP+bTe7TJf/32XHaizjAnJIpZIRHMCYliVkgEc0Lu8IlZx88lrVbb6e0Gg6FP9xAPHjwYJSUlnd5XXl7OQ1GIiIiIiIjOUxdco93Y2HhO1nP48OFzsh4iIiIiIiLyLTx0nDyC1xwkEcwJiWJWSARzQqKYFRLBnJA72GiTR3COPRLBnJAoZoVEMCckilkhEcwJuYONNnlEZWWlt0sgP8CckChmhUQwJySKWSERzAm5g402ERERERERUR9io00ewWsOkgjmhEQxKySCOSFRzAqJYE7IHQqJJx/4pZ4ukE5ERERERESe0VM/xj3a5BE1NTXeLoH8AHNCopgVEsGckChmhUQwJ+QONtrkERaLxdslkB9gTkgUs0IimBMSxayQCOaE3MFGm4iIiIiIiKgPsdEmjwgNDfV2CeQHmBMSxayQCOaERDErJII5IXdwMjQ/xcnQiIiIiIiIvIOToZFXFBcXe7sE8gPMCYliVkgEc0KimBUSwZyQO9hok0e0tbV5uwTyA8wJiWJWSARzQqKYFRLBnJA72GgTERERERER9SE22uQRERER3i6B/ABzQqKYFRLBnJAoZoVEMCfkDqW3Czif5ObmYty4cWhtbYVSeWG/tG8dfQtByiBvl0E+rrWtlTkhIcwKiWBO3JOVkeXtEs6Z4OBgb5dAfoA5IXdc2N2gF0mShKVLl2LDhg1oaGjAiBEj8NJLL+GSSy7xdml94pfjv8BkMnm7DPJxZrOZOSEhzAqJYE5IVFlZGdLT071dBvk45oTcwUPHvWT58uV49dVX8fHHH6O2thajR4/GhAkT0NjY6O3SiIiIiIiIyA1e36OdmZmJYcOG4cSJE9ixYwd0Oh0WL16MOXPmYOPGjViyZInL9cmWLl2Kzz77DLt375bHDxkyBDU1Ndi1axd0Oh2WL1+OwYMH495778WhQ4dw8cUXY9OmTRgwYECP9ZjNZixZsgS7du1CbW0t4uLisHLlSkyYMAFOpxPZ2dnYsGEDqqur0b9/fzz11FO44YYbev28X3rpJSxcuBBDhgwBADz11FP4v//7P+Tk5OC//uu/znh8dnY2srOz5Z+tVivy8/MBnDqspeM6bna7HQBgMpmg1+tRVFQkj4mPj0dLSwtqamoAAIGBgUhJSUFVVZXc4IeFhSEqKgqFhYVob28HAERFRUGpVKK8vFxeVkpKCurr61FXV+dSQ1lZGRwOB5qbm6HRaKBWq1FfXy+P0+l0cDqdaGpqAgAEBATAYDDAarWitbUVAKBWqxEaGgqz2SyP02q1UCgUsFqt8m1GoxE2m01+zkqlEjqdDg0NDXA6nQAAjUYDlUqFhoYGlxra2trQ3NzcJzU0NzfD4XAAAIKCghAWFob6+nr59QsJCUFQUJBLDXq9Hq2trT3WEBISIr/GHe+PJEkuX8iYTCY0NTX1WINSqYTFYnGpoaWlBTabTc6DXq+HxWKRZ9kMDg6GRqPpkxpCQ0MRGBjoUkN7eztsNluPNQQHB7vkKCwsDO3t7XKOFAoFjEYjGhsb0dLSAgBQqVTQarWoq6uDJEld1mAwGOBwOOQaOnJ0eg0iWe5NDQEBAS45MhgMsNvtPWb5Qt6eOpbP7anr7em3Wb4Qt6fm5mZuT6fV0NvPp/z8fCQmJqK5uRm1tbXyuKSkJFRUVMh16fV6hIeHo6CgQF5fbGwsJElCZWWl/B6mpaWhpqZGzkhoaChiYmJQXFws5yEiIgLBwcEoKyuTl5WUlITGxkacPHlSfu8TExNRXl4uZ8tgMMBkMqGwsFAeFxcXB6fTiaqqqh5rMJvN8t9RkZGRUKvVLjUkJyfDYrHIr7NarUZCQoLL31pGoxEGg8Hlb624uDi0tbWhurpafg9TU1NRXV0tvz9arRbR0dEoKiqS8xAZGQmVSuXyN29KSgoaGhrkGnzx772OGnQ6nculsBISEuBwOOQalEolkpOTUVlZKW9jOp0OkZGRKCgokLfp6OhoBAYGutSQmpoKs9ksb68ajQZxcXEoLS2Vfz/069cPWq0WJSUlLjXY7XacOHGiVzXExMRAoVCgoqJC3g7S0tJQW1srbyshISGIjY1FSUmJvG2Gh4cjJCQEpaWlcg0XyvZ0eg0X0vbUE4XUkSovyczMxA8//ICcnBxkZmYiJycHU6ZMwdGjR7F7926hRjsvLw/bt2/HmDFjsGrVKixevBiZmZlYs2YNYmNjMXXqVDQ2NuLjjz/uthZJkjB27FhotVqsXbsWCQkJKCoqgt1ux6BBg/D8889jxYoV2L59O4YOHYpt27bhrrvuwt69e5GRkSF8jnZDQwMMBgP27NmDK6+8Ur79uuuuwyWXXOLSUHelpwuke9tfPv8LAgMDvV0G+Tin08mckBBmhUQwJ+5ZmrnU2yWcM62trQgK4vn81D3mhLrTUz/m9T3aADB58mSMHz9e/r/JZML+/ft7Nf6qq64CAMycORMPP/wwZsyYgeTkZADAtGnTkJXV8wQf+/fvx+7du1FdXS3PMpiamirfv379ejz66KPIyMgAANx5553YvHkz1q9fj7Vr1wrX2/EtkMFgcLndaDS67BnwZ+GqcISGhnq7DPJxTU1NzAkJYVZIBHNCohobG2E0Gr1dBvk45oTc4RONdmxsrMvPoaGhsFqtwjN3x8TEuIzt7LbTD6fqSlFREYxGY5dT+ZeVlSEtLc3ltvT0dPzyyy9CdXbQ6XQA4HLIGgDU1dUhLi6uV8vyVTfG3MjJI6hH+fn5zAkJYVZIBHNCok6ePMkGinrEnJA7fHoytLCwMPkcig4VFRUeW19ycjLq6urk8yh+KyEhweX8CQAoKChAYmJir9aj1+uRnJyMffv2ybe1tbUhLy8Pw4cP733hRERERERE5DN8utEePnw4rFYr3n77bbS3tyM3NxfvvPOOx9Y3cuRIjBo1CrNmzZKPty8qKpL3WGdlZWH58uXIy8tDW1sbtm7dip07dwodlv5bc+fOxfLly3Ho0CHYbDY8+eSTCAoKwh/+8Ic+fU7eolKpvF0C+QHmhEQxKySCOSFRzAqJYE7IHT7daKempmL16tVYuHAhDAYD1q1bh1mzZnlsfQqFAh988AFiYmJw5ZVXIiwsDBMnTpRnzVuwYAHmzZuH22+/HSaTCcuWLcN7772HkSNH9npdCxcuxMyZM3HNNdegX79++Oabb/DRRx9Bq9X29dPyit7u5acLE3NCopgVEsGckChmhUQwJ+QOr886TmfH12cdLy8vP2/ONyfPYU5IFLNCIpgTEsWskAjmhLrTUz/m03u0yX91XJePqDvMCYliVkgEc0KimBUSwZyQO3xi1vFzqatDsw0GQ5/uIR48eDBKSko6va+8vBx6vb7P1kVERERERES+44JrtBsbG8/Jeg4fPnxO1uOrfnuNcKLOMCckilkhEcwJiWJWSARzQu7goePkESaTydslkB9gTkgUs0IimBMSxayQCOaE3MFGmzyisLDQ2yWQH2BOSBSzQiKYExLFrJAI5oTcwUabiIiIiIiIqA+x0SYiIiIiIiLqQ2y0ySN4zUESwZyQKGaFRDAnJIpZIRHMCbmDjTZ5hNPp9HYJ5AeYExLFrJAI5oREMSskgjkhd7DRJo+oqqrydgnkB5gTEsWskAjmhEQxKySCOSF3sNEmIiIiIiIi6kNstMkjFAqFt0sgP8CckChmhUQwJySKWSERzAm5QyFJkuTtIqj34uPjcfz4cW+XQUREREREdMHpqR/jHm3yiJqaGm+XQH6AOSFRzAqJYE5IFLNCIpgTcgcbbfIIi8Xi7RLIDzAnJIpZIRHMCYliVkgEc0LuYKNNRERERERE1IfYaJNHhIaGersE8gPMCYliVkgEc0KimBUSwZyQOzgZmp/iZGhERERERETewcnQyCuKi4u9XQL5AeaERDErJII5IVHMColgTsgdSm8XcL7ZuHEjlixZcsHvbX7ryFuINEd6uwzycTU1NcwJISsjq8fHtLW1nYNKyN8xJySKWSERzAm547xvtIuLi5GSkoJjx44hPT3d2+W4OH78OB555BHk5ubCZrPh2muvxapVqxAbG+vt0txW1VyFFkuLt8sgH2duNjMnRERERHTe4aHjXtLe3o5JkyZBo9GgoKAAx48fR2BgICZNmoTz4bR5Th5BIpgTEhUZySMfqGfMCYliVkgEc0Lu8Js92jabDX/729+wdetWVFZWIiIiAk8++SRGjBiBhx56CD/++CNaWlowcOBAPPvssxg/fjwAYPDgwQCAYcOGQaFQYMaMGVi7dm236zp+/Dgee+wxfPXVV7BYLEhNTcWrr76KjIwM2O12PPnkk9i6dSvq6+txySWXYPny5bjiiit69XyOHj2KH374ATt37oRWqwUAPPPMM+jfvz/27NmD0aNHuzw+Ozsb2dnZ8s9WqxX5+fkAgODgYPlkfLvdDgAwmUzQ6/UoKiqSx8THx6OlpQU1NTUAgMDAQKSkpKCqqgqNjY0AgLCwMERFRaGwsBDt7e0AgKioKCiVSpSXl8vLSklJQX19Perq6lxqKCsrg8PhgNVqRXt7O9RqNerr6+VxOp0OTqcTTU1NAICAgAAYDAZYrVa0trYCANRqNUJDQ2E2m+VxWq0WCoUCVqtVvs1oNMJms8nPWalUQqfToaGhAU6nEwCg0WigUqnQ0NDgUkNbWxuam5v7pIbm5mY4HA4AQFBQEMLCwlBfXy+/fiEhIQgKCnKpQa/Xo7W1tccaQkJC5Ne44/2RJEl+vzre66amph5rUCqVLteD1Ov1aGlpgc1mk/Og1+thsVjkQ6WCg4Oh0Wj6pIbQ0FAEBga61KDVamGz2XqsITg42CVHYWFhaG9vl3OkUChgNBrR2NiIlpZTe8hVKhW0Wi3q6urkL686q8FgMMDhcMg1dOTo9Bo0Gk2PWe5NDQEBAS45MhgMsNvtPWb5fN2eOn6XpaWloba2Vl5+SEgIYmNjUVJSApvNhpqaGoSHhyMkJASlpaXyshITE9Hc3Iza2loAp/KXlJSEiooKuS69Xo/w8HAUFBTI42JjYyFJEiorK+X3MC0tDTU1NXJGQkNDERMTg+LiYjkPERERCA4ORllZmbyspKQkNDY24uTJk/J7n5iYiPLycjlbBoMBJpMJhYWF8ri4uDg4nU5UVVX1qobIyEio1WqXGpKTk2GxWOTXWa1WIyEhweWzwWg0wmAwuHw2xMXFoa2tDdXV1fJ7mJqaiurqajkjWq0W0dHRKCoqkvMQGRkJlUrlcqpUSkoKGhoa5BrO9edTW1sbQkNDu/186qhBp9O5nH+ZkJAAh8Mh16BUKpGcnIzKykp5G9PpdIiMjERBQYG8TUdHRyMwMNDlMzI1NRVms1neXjUaDeLi4lBaWir/fujXrx+0Wi1KSkpcarDb7Thx4kSvaoiJiYFCoUBFRYW8rO62p45t80Lenqqrq+X3mtvT2f+911HD+bo9tbW1oaamhttTL2q4kLannvjNrOMzZszAr7/+itdeew0DBw5EZWUlqqqqoFKpUFlZiTFjxiAgIADPPvssVqxYgWPHjiEyMrLXh47bbDYMGzYMo0ePxnPPPQeTyYQjR44gNDQUSUlJmD9/PnJzc5GTk4PExESsWrUKS5cuxS+//IL4+Hjhc7R//vlnDB48GBUVFYiJiQEA/PrrrxgwYABefPFFzJ8/v9vxvj7r+IPvPQiTyeTtMsjHmc1m5oSwNHNpj4/Jz8/3udN/yPcwJySKWSERzAl1p6d+zC/2aNfW1uLNN9/Evn37cPHFFwM49U1Px7nMl1xyifzYpUuX4oUXXsB3332Hm2++udfr2rFjB06ePIm1a9dCrVYDAAYNGgTg1OHer7zyCjZv3ixvdI888ghef/11vPHGG3j88ceF13PRRRfh4osvxqJFi7B69Wo4nU488cQTUCgULnvb/FV0SDQidTzchrqnsquYEyIiIiI67/hFo92x+37AgAFn3FdaWopFixZhz549qK+vR0BAACwWi7x7/2zWlZycLDfZp6utrYXNZkNaWprL7enp6S6HiYhQKpX417/+hYULF2LgwIEICgrCokWL8MknnyA8PPysavclU9Kn8BtA6hG/KSYiIiKi85FfTIaWnJwM4NSh1b91zz33oL29Hfv27YPFYkFdXR10Op18nkVAQO+eYnJyMoqLi+VzPk4XHh6O4OBgl3MoAKCgoACJiYm9Wg9w6vypnJwcVFZWorS0FL///e/R1NQkn1/uzzreM6LuMCckilkhEcwJiWJWSARzQu7wi0Y7IiICU6dOxbx583D06FEAQGVlJQ4cOICGhgZotVoYjUY0NTXhiSeecJmkKSIiAgEBAfK4ntx0000wGo2YO3cuamtrIUkSfv75Z5SUlCAgIACzZ8/GX//6VxQWFqKlpQUrVqxAfn4+pk+f3uvn9eOPP8qTRR08eBCzZs3CnDlz0L9//14vy9ecD4e/k+cxJySKWSERzAmJYlZIBHNC7vCLRhsANmzYgLFjx+KGG26AVqvF6NGjcfjwYbz44os4ePAgjEYjBg0ahLi4OMTHx8vjNBoNnnnmGWRlZcFgMGDu3Lndrkej0eCLL75AY2MjhgwZAr1ej+nTp8sz1C1fvhzXXXcdxo0bh8jISLz77rv49NNPkZCQ0OvntH37dgwYMABarRaTJ0/GlClTsGrVql4vxxedPrswUVeYExLFrJAI5oREMSskgjkhd/jNrOPkytdnHee5tySCOSFRzAqJYE5IFLNCIpgT6k5P/Zjf7NEm/9LZZHJEv8WckChmhUQwJySKWSERzAm544Lbo11aWipfruu3hg4dij179vTZurRabae3GwwGt/dG+/oebSIiIiIiovPVeXEd7b6UmJjoMlmaJ52r9fii48ePu5wrT9QZ5oREMSskgjkhUcwKiWBOyB08dJw8wm63e7sE8gPMCYliVkgEc0KimBUSwZyQO9hoExEREREREfUhNtrkEUaj0dslkB9gTkgUs0IimBMSxayQCOaE3MFGmzzCYDB4uwTyA8wJiWJWSARzQqKYFRLBnJA72GiTRxQVFXm7BPIDzAmJYlZIBHNCopgVEsGckDvYaBMRERERERH1ITbaRERERERERH2IjTZ5RFxcnLdLID/AnJAoZoVEMCckilkhEcwJuYONNnlEW1ubt0sgP8CckChmhUQwJySKWSERzAm5g402eUR1dbW3SyA/wJyQKGaFRDAnJIpZIRHMCbmDjTYRERERERFRH2KjTR4REMBoUc+YExLFrJAI5oREMSskgjkhdygkSZK8XQT1Xnx8PI4fP+7tMoiIiIiIiC44PfVj/JqGPILntJAI5oREMSskgjkhUcwKiWBOyB1stMkjrFart0sgP8CckChmhUQwJySKWSERzAm5Q+ntAs4nubm5GDduHFpbW6FUXtgv7db8rYi0RHq7DPJxNTU1fpuTrIwsb5dARERERD7qwu4Gveh//ud/8Prrr6O2thZBQUEYMWIEli1bhksvvdTbpfUJc5sZLZYWb5dBPq6xrZE5ISFardbbJZAfYE5IFLNCIpgTcgcPHfeSO++8E99//z0aGhpQUVGB6667DhMmTIDT6fR2aX2Cv5hIBHNCoqKjo71dAvkB5oREMSskgjkhd3h9j3ZmZiaGDRuGEydOYMeOHdDpdFi8eDHmzJmDjRs3YsmSJS6zuS1duhSfffYZdu/eLY8fMmQIampqsGvXLuh0OixfvhyDBw/Gvffei0OHDuHiiy/Gpk2bMGDAgB7rMZvNWLJkCXbt2oXa2lrExcVh5cqVchOcnZ2NDRs2oLq6Gv3798dTTz2FG264odfP+/RaJElCYGAgampqYDabERERccbjs7OzkZ2dLf9stVqRn58PAAgODpZnvbPb7QAAk8kEvV6PoqIieUx8fDxaWlpQU1MDAAgMDERKSgqqqqrQ2NgIAAgLC0NUVBQKCwvR3t4OAIiKioJSqUR5ebm8rJSUFNTX16Ours6lhrKyMjgcDpSXl8NkMkGtVqO+vl4ep9Pp4HQ60dTUBODUZRMMBgOsVitaW1sBAGq1GqGhoTCbzfI4rVYLhULhcq6M0WiEzWaTn7NSqYROp0NDQ4P8hYVGo4FKpUJDQ4NLDW1tbWhubu6TGpqbm+FwOAAAQUFBCAsLQ319vfz6hYSEICgoyKUGvV6P1tbWHmsICQmRX+OO90eSJPn96nivm5qaeqxBqVTCYrG41NDS0gKbzSbnQa/Xw2KxoK2tTX5fNRpNn9QQGhqKwMBAlxo63qOeaggODnbJUVhYGNrb2+UcKRQKGI1GNDY2oqXl1B5ylUoFrVaLuro6dFxcobMaDAYDHA6HXENHjk6vQaPRnJFlu91+zranjtdYp9OhuLhYHpeQkACHwyHXoFQqkZycjMrKSvm10el0iIyMREFBgfw6REdHIzAw0KWG1NRUmM1m+TlqNBrExcWhtLRUfk379esHrVaLkpISlxrsdjtOnDjRqxpiYmKgUChQUVEhLystLQ21tbXythISEoLY2FiUlJSguroaJpMJ4eHhCAkJQWlpqTwuMTERzc3NqK2tBXAqf0lJSaioqJC3Mb1ej/DwcBQUFMjjYmNjIUkSKisrAZzKUVpaGmpqauSMhIaGIiYmBsXFxXIeIiIiEBwcjLKyMnlZSUlJaGxsxMmTJwGcyl9iYiLKy8vlbBkMBphMJhQWFsrj4uLi4HQ6UVVV1asaIiMjoVarXWpITk6GxWKRf2+p1WokJCS4fDYYjUYYDAaXz4a4uDi0tbXJk/4EBAQgNTUV1dXV8u87rVaL6OhoFBUVyb9fIyMjoVKpXD6jU1JS0NDQINdwrj+fzGYzRowYwe0J3W9PHZ8zF/L29P3338NgMADg9sTPp663J7PZDJPJxO2pFzVcSNtTT7x+ea/MzEz88MMPyMnJQWZmJnJycjBlyhQcPXoUu3fvFmq08/LysH37dowZMwarVq3C4sWLkZmZiTVr1iA2NhZTp05FY2MjPv74425rkSQJY8eOhVarxdq1a5GQkICioiLY7XYMGjQIzz//PFasWIHt27dj6NCh2LZtG+666y7s3bsXGRkZvT5He8eOHZg+fToaGhqgUCjw8MMPuzTT3fH1y3s9+N6DMJlM3i6DfFzHB5g/Wpq51NslXFDy8/ORnp7u7TLIxzEnJIpZIRHMCXWnp37M63u0AWDy5MkYP368/H+TyYT9+/f3avxVV10FAJg5cyYefvhhzJgxA8nJyQCAadOmISur54mL9u/fj927d6O6ulreq5yamirfv379ejz66KPIyMgAcOrw782bN2P9+vVYu3atcL0dbrzxRtTX18NsNuO1115DfHx8r5fhq6JDohGp889JrujcUdlVzAkRERERnXd8otGOjY11+Tk0NBRWq1V45u6YmBiXsZ3dJjI9f1FREYxGY6eHbgNAWVkZ0tLSXG5LT0/HL7/8IlRnV0wmEx566CEYjUZcdNFFGDZsmFvL8wUPjHoAOp3O22WQj7NYLMwJCYmM5Bcy1DPmhEQxKySCOSF3+PRkaGFhYfI5FB1OP/+oryUnJ6Ourk4+j+K3EhISXM6fAICCggIkJia6ve729na0trbi2LFjbi/LF6hUKm+XQH6AOSFRzAqJYE5IFLNCIpgTcodPN9rDhw+H1WrF22+/jfb2duTm5uKdd97x2PpGjhyJUaNGYdasWfLx9kVFRfIe66ysLCxfvhx5eXloa2vD1q1bsXPnTqHD0n9r5cqV8on9J06cwNy5c6FSqTB69Oi+e0Je5Mvnj5PvYE5IFLNCIpgTEsWskAjmhNzh0412amoqVq9ejYULF8JgMGDdunWYNWuWx9anUCjwwQcfICYmBldeeSXCwsIwceJEeda8BQsWYN68ebj99tthMpmwbNkyvPfeexg5cmSv1/Xpp59i6NChCA0NxdChQ1FVVYXPPvvM5ZB3IiIiIiIi8j9en3Wczo6vzzrOWRpJBHNCopgVEsGckChmhUQwJ9SdnvoxNtp+ytcbbafTicDAQG+XQT6OOSFRzAqJYE5IFLNCIpgT6k5P/ZhPHzruCVqtttN/fX1prcGDB3e5ro6L3Z/PLoTnSO5jTkgUs0IimBMSxayQCOaE3OETl/c6lxobG8/Jeg4fPnxO1uOrzGYzTCaTt8sgH8eckChmhUQwJySKWSERzAm544Lbo01ERERERETkSWy0ySOCg4O9XQL5AeaERDErJII5IVHMColgTsgdnAzNT/n6ZGhERERERETnK06GRl7BLwFIBHNCopgVEsGckChmhUQwJ+QONtrkEXa73dslkB9gTkgUs0IimBMSxayQCOaE3MFGm4iIiIiIiKgPsdEmj+ClEEgEc0KimBUSwZyQKGaFRDAn5A422uQRer3e2yWQH2BOSBSzQiKYExLFrJAI5oTcwUabPKKoqMjbJZAfYE5IFLNCIpgTEsWskAjmhNzBRpuIiIiIiIioD7HRJiIiIiIiIupDbLTJI+Lj471dAvkB5oREMSskgjkhUcwKiWBOyB1stMkjWlpavF0C+QHmhEQxKySCOSFRzAqJYE7IHWy0ySNqamq8XQL5AeaERDErJII5IVHMColgTsgdbLSJiIiIiIiI+hAb7V7Izc2FQqFAW1ubt0vxeYGBgd4ugfwAc0KimBUSwZyQKGaFRDAn5A6ltws4X23ZsgVr1qzBwYMHYbVa0draCqWy85d7//79+N3vfocrrrgCu3fvPseVesbndZ8Ddd6ugs6lrIysXo9JSUnxQCV0PmJWSARzQqKYFRLBnJA72Gh7iNFoxNy5c2Gz2fCnP/2py8fZ7XbMnDkTY8eOhd1uP4cVetaRiiPQarXeLoN8XFVVFaKjo71dBvkBZoVEMCckilkhEcwJucPjh45nZmbioYcewrRp06DX65GQkICXX34ZALBx48Yzps1funQpxowZ4zJ+/vz5uOOOO6DT6RAfH48tW7bgp59+wpVXXomwsDBcfvnlOHr0qFA9ZrMZc+fORUpKCsLCwjBw4EB8/PHHAACn04nnnnsOF110EfR6PUaOHIldu3ad1fOeMGECpk6ditTU1G4f99///d+4+uqrXZ7z+YCzNJKIxsZGb5dAfoJZIRHMCYliVkgEc0LuOCd7tDdu3IicnBy88cYbyMnJwZQpU3DttdcKj9+0aRO2b9+OzZs3Y9WqVfjTn/6EzMxMbN68GbGxsZg6dSoefPBBuWHuiiRJuPXWW6HVavHVV18hISEBRUVF8p7kF154AStXrsT27dsxdOhQbNu2Dbfccgv27t2LjIwMt16Dznz99df48MMP8cMPP+Af//hHt4/Nzs5Gdna2/LPVakV+fj4AIDg4GPHx8Th+/Lj8XEwmE/R6PYqKiuQx8fHxaGlpkWdQDAwMREpKCqqqquRfJGFhYYiKikJhYSHa29sBAFFRUVAqlSgvL5eXlZKSgvr6etTV1bnUUFZWBofDgebmZmg0GqjVatTX18vjdDodnE4nmpqaAAABAQEwGAzy4fUAoFarERoaCrPZLI/TarVQKBSwWq3ybUajETabTX7OSqUSOp0ODQ0NcDqdAACNRgOVSoWGhgaXGtra2tDc3NwnNTQ3N8PhcAAAgoKCEBYWhvr6evn1CwkJQVBQkEsNer0era2tPdYQEhIiv8Yd748kSS6/+E0mE5qamnqsQalUwmKxuNTQ0tICm80m50Gv18NiscjzEAQHB0Oj0QjVUFNTIy8/NDQUMTExKC4ulpcVGRkJtVqNsrIyeZzT6YTZbJZfZ7VajYSEBJcsG41GGAwGlyzHxcWhra0N1dXV8uuXmpqK6upq+f3RarWIjo5GUVGRnIfIyEioVCocP35cXlZKSgoaGhrkGnxxe+qoQafTobi4WB6XkJAAh8Mh16BUKpGcnIzKykp5G9PpdIiMjERBQQEkSQIAREdHIzAw0KWG1NRUmM1meXvVaDSIi4tDaWmp/MVZv379oNVqUVJS4lKD3W7HiRMnelVDTEwMFAoFKioq5GWlpaWhtrZW3lZCQkIQGxuLkpIS1NbWAgDCw8MREhKC0tJSeVxiYiKam5vlxwQFBSEpKQkVFRXyNqbX6xEeHo6CggJ5XGxsLCRJQmVlJQBAoVAgLS2txyxHREQgODjYJctJSUlobGzEyZMnAQAqlQqJiYkoLy+XtzGDwQCTyYTCwkJ5XFxcHJxOJ6qqqnpVQ2fbU3JyMiwWywW9PdXW1nJ7+v91tz11fM5cyNuT2WyW/47i9sTPp662p47tgNsTP5862556opA6UuUhmZmZSE1NxauvvirfFhERgdWrV8Nms2HJkiUuL8LSpUvx2WefyecqZ2ZmIi0tDa+88goAoKGhAQaDAW+99RamTp0KAHj33XeRlZXl0gx05vvvv8fll1+O6upqREREnHH/gAEDMHfuXDz00EPybbfccgtiYmKwdu1a5ObmYty4cd2eb/1bXY1pbGzEpZdeildffRVXXXXVGc+7Jx3B8FULP1zIQ8cvMEszl/Z6THV1NaKiovq+GDrvMCskgjkhUcwKiWBOqDs99WPnZI92bGysy8+hoaGwWq3CzWpMTIzL2M5uO30vY1eKiopgNBo7bbIBoKysDGlpaS63paen45dffhGqszcWLlyIiRMn4qqrrurzZfuCgbEDvV0C+QF+eJEoZoVEMCckilkhEcwJucOrk6GFhYXJh250OP2wp76WnJyMuro61NbWIjw8/Iz7ExISXA7bAICCggIkJib2eS0fffQR6uvr8dZbbwEAmpub0draivDwcOzduxfp6el9vs5zabxhfI/npxMVFhYyJySEWSERzAmJYlZIBHNC7vDqdbSHDx8Oq9WKt99+G+3t7cjNzcU777zjsfWNHDkSo0aNwqxZs+Td/EVFRfIe66ysLCxfvhx5eXloa2vD1q1bsXPnTmRl9f6yRU6nE3a7XT53xOFwwG63y+fB7N27F4cOHUJeXh7y8vJw//33Y/jw4cjLy0NycnLfPGEv6nieRN1hTkgUs0IimBMSxayQCOaE3OHVRjs1NRWrV6/GwoULYTAYsG7dOsyaNctj61MoFPjggw8QExMjz1g+ceJE+WT9BQsWYN68ebj99tthMpmwbNkyvPfeexg5cmSv17Vp0yZoNBpMmDABwKkT9TUaDb7++msApyZ7iI+Pl//pdDqoVCrEx8cLH1JPREREREREvsfjk6GRZ/j6ZGhWqxVhYWHeLoN8HHNCopgVEsGckChmhUQwJ9Sdnvoxr+7RpvMX98qTCOaERDErJII5IVHMColgTsgd512jrdVqO/0XHx/fp+sZPHhwl+s6/XrJF6rTr39I1BXmhEQxKySCOSFRzAqJYE7IHefd1zSNjY3nZD2HDx8+J+shIiIiIiIi/3Le7dEmIiIiIiIi8iZOhuanfH0yNKfTicDAQG+XQT6OOSFRzAqJYE5IFLNCIpgT6g4nQyOvqK+v93YJ5AeYExLFrJAI5oREMSskgjkhd7DRJo+oq6vzdgnkB5gTEsWskAjmhEQxKySCOSF3sNEmIiIiIiIi6kNstMkjgoODvV0C+QHmhEQxKySCOSFRzAqJYE7IHZwMzU/5+mRoRERERERE5ytOhkZeUVZW5u0SyA8wJySKWSERzAmJYlZIBHNC7mCjTR7hcDi8XQL5AeaERDErJII5IVHMColgTsgdbLSJiIiIiIiI+hAbbfIIk8nk7RLIDzAnJIpZIRHMCYliVkgEc0LuYKNNHqHT6bxdAvkB5oREMSskgjkhUcwKiWBOyB1stMkjiouLvV0C+QHmhEQxKySCOSFRzAqJYE7IHWy0iYiIiIiIiPoQG20iIiIiIiKiPsRGmzwiISHB2yWQH2BOSBSzQiKYExLFrJAI5oTcofR2Af5k48aNWLJkCY4fP+7tUnze+u/XQ6PReLsMr8jKyPJ2CX7D4XBArVZ7uwzyA8wKiWBOSBSzQiKYE3KHXzfaxcXFSElJwbFjx5Cenu7tcly8+OKLePPNN/HTTz/BZDJ12pxv3LgRzz33HEpKShATE4MnnngCs2fP9kK1fe9Y9TFeEoF6VFNTwxk9SQizQiKYExLFrJAI5oTc4deNti+LjY3FokWLcOTIEbz88stn3J+Tk4OHHnoIu3btwu9+9zt89dVXuPHGGxEeHo5JkyZ5oWIiIiIiIiLqCz7RaNtsNvztb3/D1q1bUVlZiYiICDz55JMYMWIEHnroIfz4449oaWnBwIED8eyzz2L8+PEAgMGDBwMAhg0bBoVCgRkzZmDt2rXdruv48eN47LHH8NVXX8FisSA1NRWvvvoqMjIyYLfb8eSTT2Lr1q2or6/HJZdcguXLl+OKK67o9XO6/fbbAZzaa92ZLVu2YOrUqRg1ahQAYNy4cbjtttuwatWqThvt7OxsZGdnyz9brVbk5+cDAIKDgxEfH4/jx4/DbrcDAEwmE/R6PYqKiuQx8fHxaGlpQU1NDQAgMDAQKSkpqKqqQmNjIwAgLCwMUVFRKCwsRHt7OwAgKioKSqUS5eXl8rJSUlJQX1+Puro6lxrKysrgcDhgs9lgs9mgVqtRX18vj9PpdHA6nWhqagIABAQEwGAwwGq1orW1FQCgVqsRGhoKs9ksj9NqtVAoFLBarfJtRqMRNptNfs5KpRI6nQ4NDQ1wOp0AAI1GA5VKhYaGBpca2tra0Nzc3Cc1NDc3w+FwAACCgoIAACUlJfKywsPDERISgtLSUnlcYmIimpubUVtbK49LSkpCRUWFXJder0d4eDgKCgrkcbGxsZAkCZWVlQAAhUKBtLQ01NTUwGKxAABCQ0MRExOD4uJitLW1AQAiIiIQHByMsrIyeVlJSUlobGzEyZMnAQAqlQqJiYkoLy+HzWYDABgMBphMJhQWFsrj4uLi4HQ6UVVV1asaIiMjoVarXWpQKBQwm83y66xWq5GQkOCSZaPRCIPB4JLluLg4tLW1obq6Wn4PU1NTUV1dLb8/Wq0W0dHRKCoqkvMQGRkJlUrlcoRJSkoKGhoa5Bp8cXvqqEGn07lcaiQhIQEOh0OuQalUIjk5GZWVlfI2ptPpEBkZiYKCAkiSBACIjo5GYGCgSw2pqakwm83y9qrRaBAXF4fS0lK0tLQAAPr16wetVouSkhKXGux2O06cONGrGmJiYqBQKFBRUSEvKy0tDbW1tfL2GhISgtjYWJSUlKCurg75+fncnrrZnpKTk2GxWC7o7amurg5Op5PbE7rfnvj5FIOGhgb57yhuT/x86mp76vjs4fbEz6fOtqeeKKSOVHnRjBkz8Ouvv+K1117DwIEDUVlZiaqqKqhUKlRWVmLMmDEICAjAs88+ixUrVuDYsWOIjIzs9aHjNpsNw4YNw+jRo/Hcc8/BZDLhyJEjCA0NRVJSEubPn4/c3Fzk5OQgMTERq1atwtKlS/HLL78gPj7+rM7R7mrMlClTYDQasW7dOvm2adOm4eOPP5Y3hO50BMNXLc1d6u0SvGZp5lJvl0BERERERB7UUz/m9T3atbW1ePPNN7Fv3z5cfPHFAE59ixMbGwsAuOSSS+THLl26FC+88AK+++473Hzzzb1e144dO3Dy5EmsXbtWnthg0KBBAID29na88sor2Lx5s9y0P/LII3j99dfxxhtv4PHHH3fref7Wrbfeivvvvx/Tp0/HqFGj8OWXX+L999+XvxXzd1pJC4Pe4O0yyMdVVlYiJibG22WQH2BWSARzQqKYFRLBnJA7vN5od+yaHzBgwBn3lZaWYtGiRdizZw/q6+sREBAAi8Ui77o/m3UlJyd3OntgbW0tbDYb0tLSXG5PT093OQSkr0ybNg1msxlz5sxBRUUFRo4ciXvvvRdvv/12n6/LG25NuNXnJqgj39Nx6BZRT5gVEsGckChmhUQwJ+QOr19HOzk5GQDw66+/nnHfPffcg/b2duzbtw8WiwV1dXXQ6XTyORQBAb0rPzk5GcXFxfL5HKcLDw9HcHCwy/kRAFBQUIDExMRerUfUAw88gMOHD6Ourg6ffvopSkpKcPXVV3tkXURERERERHRueL3RjoiIwNSpUzFv3jwcPXoUwKnDNA4cOICGhgZotVoYjUY0NTXhiSeekCdx6BgbEBAgj+vJTTfdBKPRiLlz56K2thaSJOHnn39GSUkJAgICMHv2bPz1r39FYWEhWlpasGLFCuTn52P69Om9fl5tbW2w2+3yoeB2ux12u13+ksBqteKnn35Ce3s7LBYLnn/+eeTm5uLJJ5/s9bp8ES+FQCKYExLFrJAI5oREMSskgjkhd3i90QaADRs2YOzYsbjhhhug1WoxevRoHD58GC+++CIOHjwIo9GIQYMGIS4uDvHx8fI4jUaDZ555BllZWTAYDJg7d26369FoNPjiiy/Q2NiIIUOGQK/XY/r06fLsc8uXL8d1112HcePGITIyEu+++y4+/fRTJCQk9Po5Pf3009BoNLj33ntRXl4OjUYDjUYjz4ZosVgwY8YM6PV6xMfH44svvsA333yD/v3793pdvigyMtLbJZAfYE5IFLNCIpgTEsWskAjmhNzhE7OOU+/5+qzjBQUFZ5zvTvRbzAmJYlZIBHNCopgVEsGcUHd66sd8Yo82nX/4/Q2JYE5IFLNCIpgTEsWskAjmhNzh9VnH+1Jpaal8ua7fGjp0KPbs2dNn69JqtZ3ebjAYfHpPMxEREREREXnWedVoJyYmukyW5knnaj3+Kjo62tslkB9gTkgUs0IimBMSxayQCOaE3MFDx8kjAgMDvV0C+QHmhEQxKySCOSFRzAqJYE7IHWy0ySPKy8u9XQL5AeaERDErJII5IVHMColgTsgdbLSJiIiIiIiI+hAbbSIiIiIiIqI+xOto+ylfv452e3s7AgL4PQ51jzkhUcwKiWBOSBSzQiKYE+oOr6NNXmE2m71dAvkB5oREMSskgjkhUcwKiWBOyB1stMkj6uvrvV0C+QHmhEQxKySCOSFRzAqJYE7IHWy0iYiIiIiIiPoQG23yCI1G4+0SyA8wJySKWSERzAmJYlZIBHNC7uBkaH7K1ydDIyIiIiIiOl9xMjTyitLSUm+XQH6AOSFRzAqJYE5IFLNCIpgTcgcbbfKIlpYWb5dAfoA5IVHMColgTkgUs0IimBNyBxttIiIiIiIioj7ERps8ol+/ft4ugfwAc0KimBUSwZyQKGaFRDAn5A422uQRWq3W2yWQH2BOSBSzQiKYExLFrJAI5oTcwUa7FzZu3Ij4+Hhvl+EXSkpKvF0C+QHmhEQxKySCOSFRzAqJYE7IHUpvF+CO4uJipKSk4NixY0hPT/d2OS5efPFFvPnmm/jpp59gMpk6nfo9NzcXCxYswJEjRxAVFYVFixZhzpw5Xqi2723N34pIS6S3y/CYrIwsb5dAREREREQ+yq8bbV8WGxuLRYsW4ciRI3j55ZfPuL+kpAQ33ngj/vGPf+Cee+7Bv//9b0yaNAnR0dH4wx/+4IWK+1ZVcxVaLJypkYiIiIiILjw+cei4zWbDkiVLcNFFFyEsLAypqal47bXXcOjQIVx99dWIiIiAXq/HFVdcgS+++EIeN3jwYADAsGHDoNVqcf/99/e4ruPHj2P69OmIj4+HTqfDpZdeigMHDgAA7HY7HnvsMaSkpMBoNOL3v/89vvvuu7N6TrfffjsmT56MuLi4Tu/fuHEjLrroIsybNw8qlQpjx47F7NmzsXr16rNan6/R6XTeLoH8QEJCgrdLID/BrJAI5oREMSskgjkhd/jEHu177rkHv/76Kz744AMMHDgQlZWVqKqqAgA8/vjjGDNmDAICAvDss8/iD3/4A44dO4bIyEgcPnwYKSkpOHjwoNCh4zabDePHj8fo0aORl5cHk8mEI0eOIDQ0FADw6KOPIjc3F59++ikSExOxatUqXHPNNfjll1/6/NzsvLw8XH755S63XXbZZXjttdc6fXx2djays7Pln61WK/Lz8wEAwcHBiI+Px/Hjx2G32wEAJpMJer0eRUVF8pj4+Hi0tLSgpqYGABAYGIiUlBRUVVWhsbERABAWFoaoqCgUFhaivb0dABAVFQWlUony8nJ5WSkpKaivr0ddXZ1LDWVlZXA4HDCbzQgLC4NarUZ9fb08TqfTwel0oqmpCQAQEBAAg8EAq9WK1tZWAIBarUZoaCjMZrM8TqvVQqFQwGq1yrcZjUbYbDb5OSuVSuh0OjQ0NMDpdAIANBoNVCoVGhoaXGpoa2tDc3PzWdfQ8dqnpaWhtrZWXn5ISAhiY2NRUlIiLys8PBwhISEoLS2Vl5WYmIjm5mbU1tYCAIKCgpCUlISKigq5Lr1ej/DwcBQUFMjjYmNjIUkSKisrAQAKhQJpaWmoqamBxWIBAISGhiImJgbFxcVoa2sDAERERCA4OBhlZWXyspKSktDY2IiTJ08CAFQqFRITE1FeXg6bzQYAMBgMMJlMKCwslMfFxcXB6XTK26hoDZGRkVCr1S41GI1GNDU1ya+zWq1GQkKCS5aNRiMMBoNLluPi4tDW1obq6mr5PUxNTUV1dbWcEa1Wi+joaBQVFcl5iIyMhEqlcjmVIyUlBQ0NDXINvrg9ddSg0+lQXFwsj0tISIDD4ZBrUCqVSE5ORmVlpbyN6XQ6REZGoqCgAJIkAQCio6MRGBjoUkNqairMZrO8vWo0GsTFxaG0tFS+jmi/fv2g1WpdzllLSEiA3W7HiRMnelVDTEwMFAoFKioq5GV1tz1ZrVYEBwdze+pme0pOTobFYrmgtye73Y6LL76Y2xP4+dRTDYWFhQgKCgLA7YmfT11vT3a7HcHBwdyeelHDhbQ99UQhdaTKS2praxEREYF9+/Zh5MiRPT7eYDBg06ZNuPnmm3t9jva2bdtw3333oaKiAmq12uW+9vZ2aLVabN68Gbfccot8+7BhwzB16lQ8/vjj2LhxI5YsWdLp+dZd6WrM1VdfjZEjR2LZsmXybbt27cLNN98sB7U7HcHwVQ++9yBMJpO3y/CYpZlLvV3CeSE/P9/n5lcg38SskAjmhEQxKySCOaHu9NSPeX2Pdsc3BgMGDDjjvtLSUixatAh79uxBfX09AgICYLFY5G8UzmZdycnJZzTZwKmG32azIS0tzeX29PR0l2+m+opOp3PZ0wsAdXV1580h19Eh0YjUnb+ToREREREREXXF6412cnIyAODXX3/FiBEjXO675557oNfrsW/fPkRFRUGSJBiNRvnQjoCA3p1inpycjOLiYrS0tEClUrncFx4ejuDgYBQUFOCSSy6Rby8oKMBll112Fs+se5deeik++OADl9u+//57DB8+vM/X5Q3TBk6T31uiriiVXv8VRH6CWSERzAmJYlZIBHNC7vD6ZGgRERGYOnUq5s2bh6NHjwIAKisrceDAATQ0NECr1crncT7xxBPyuSUdYwMCAuRxPbnppptgNBoxd+5c1NbWQpIk/PzzzygpKUFAQABmz56Nv/71rygsLERLSwtWrFiB/Px8TJ8+vdfPq62tDXa7XT5vw263w263y18SzJw5U56RvKWlBd988w1effVVzJs3r9fr8kVsskkEc0KimBUSwZyQKGaFRDAn5A6vN9oAsGHDBowdOxY33HADtFotRo8ejcOHD+PFF1/EwYMHYTQaMWjQIMTFxblMSqbRaPDMM88gKysLBoMBc+fO7XY9Go0GX3zxBRobGzFkyBDo9XpMnz5dPil++fLluO666zBu3DhERkbi3XffxaeffnpWMw4+/fTT0Gg0uPfee1FeXg6NRgONRiNP0pCUlISdO3di/fr10Ov1+K//+i8888wzuO2223q9Ll/UMXkDUXeYExLFrJAI5oREMSskgjkhd3h9MjQ6O74+GRonjyARzAmJYlZIBHNCopgVEsGcUHd66sd8Yo82ERERERER0fnivDrDv7S0FIMGDer0vqFDh2LPnj19ti6tVtvp7QaDwaf3NJ8r58vs6eRZzAmJYlZIBHNCopgVEsGckDt46Lif8vVDx4mIiIiIiM5XPHScvKKgoMDbJZAfYE5IFLNCIpgTEsWskAjmhNzBRps8ggdKkAjmhEQxKySCOSFRzAqJYE7IHWy0iYiIiIiIiPoQG23yiJiYGG+XQH6AOSFRzAqJYE5IFLNCIpgTcgcbbfIIhULh7RLIDzAnJIpZIRHMCYliVkgEc0LuYKNNHlFRUeHtEsgPMCckilkhEcwJiWJWSARzQu5go01ERERERETUh9hoExEREREREfUhhcR56/1STxdI9zZJknheC/WIOSFRzAqJYE5IFLNCIpgT6k5P/Rj3aJNH1NbWersE8gPMCYliVkgEc0KimBUSwZyQO9hok0c0NDR4uwTyA8wJiWJWSARzQqKYFRLBnJA72GgTERERERER9SE22uQRISEh3i6B/ABzQqKYFRLBnJAoZoVEMCfkDk6G5qd8fTI0IiIiIiKi81WfTYZWVlaGlpYWAMC3336L1atXw2q1ul8hnZdKSkq8XQL5AeaERDErJII5IVHMColgTsgdwo32Lbfcgvb2dpSXl+POO+/Et99+i9mzZ3uyNvJjra2t3i6B/ABzQqKYFRLBnJAoZoVEMCfkDmVvHhwcHIwdO3bgvvvuw5IlSzBs2DBP1eWXcnNzMW7cOLS2tkKp7NVLe97Zmr8VkZZIb5fRZ7IysrxdAhERERER+QnhbtDhcMDhcODTTz/Fww8/7MGSLgzV1dX485//jM8//xwOhwMXX3wx/v73v2Ps2LHeLq1PWGBBi6XF22WQjwsPD/d2CeQnmBUSwZyQKGaFRDAn5A7hQ8enTp2K6OholJaWYtSoUaisrORMfG6YO3cuysrKcOjQIZw8eRK33347brrpJpjNZm+X1ieCgoK8XQL5Af4OIVHMColgTkgUs0IimBNyh/Ae7SVLluCBBx6ATqeDQqFAWFgYtm3b5nYBmZmZGDZsGE6cOIEdO3ZAp9Nh8eLFmDNnDjZu3IglS5a4zOa2dOlSfPbZZ9i9e7c8fsiQIaipqcGuXbug0+mwfPlyDB48GPfeey8OHTqEiy++GJs2bcKAAQN6rMdsNmPJkiXYtWsXamtrERcXh5UrV2LChAlwOp3Izs7Ghg0bUF1djf79++Opp57CDTfc0OvnnZ+fj9mzZyMiIgIAcN9992HhwoU4duwYrrjiijMen52djezsbPlnq9WK/Px8AKcO6e+Y9c5utwMATCYT9Ho9ioqK5DHx8fFoaWlBTU0NACAwMBApKSmoqqpCY2MjACAsLAxRUVEoLCxEe3s7ACAqKgpKpRLl5eXyslJSUlBfX4+6ujqXGsrKyuBwOFBZWYl+/fpBrVajvr5eHqfT6eB0OtHU1AQACAgIgMFggNVqlc+DUavVCA0NdfnSQavVQqFQuEzAZzQaYbPZ5OesVCqh0+nQ0NAAp9MJANBoNFCpVGhoaHCpoa2tDc3NzcI15OfnIyYmBgqFAhUVFfKy0tLSUFtbKy8/JCQEsbGxKCkpkZcVHh6OkJAQlJaWyuMSExPR3NyM2tpaAKe+mEhKSkJFRYVcl16vR3h4OAoKCuRxsbGxkCQJlZWVAACFQoG0tDTU1NTAYrEAAEJDQxETE4Pi4mK0tbUBACIiIhAcHIyysjJ5WUlJSWhsbMTJkycBACqVComJiSgvL4fNZgMAGAwGmEwmFBYWyuPi4uLgdDpRVVXVqxoiIyOhVqtdanA6nYiIiJBfZ7VajYSEBJcsG41GGAwGlyzHxcWhra0N1dXV8nuYmpqK6upqOSNarRbR0dEoKiqS8xAZGQmVSuXyOyUlJQUNDQ1yDb64PXXUoNPpUFxcLI9LSEiAw+GQa1AqlUhOTkZlZaW8jel0OkRGRqKgoAAdF5mIjo5GYGCgSw2pqakwm83y9qrRaBAXF4fS0lJ5Isx+/fpBq9W6TA6TkJAAu92OEydO9KqG3m5PlZWVCA8P5/bUzfaUnJwMi8VyQW9PtbW1uOyyy7g9gZ9PPdWQl5cHk8kEgNsTP5+63p5qa2sRHh7O7akXNVxI21NPhC/v1dbWhpUrV6KgoAAvvfQSCgoKUFJSgvHjx4sM71JmZiZ++OEH5OTkIDMzEzk5OZgyZQqOHj2K3bt3CzXaeXl52L59O8aMGYNVq1Zh8eLFyMzMxJo1axAbG4upU6eisbERH3/8cbe1SJKEsWPHQqvVYu3atUhISEBRURHsdjsGDRqE559/HitWrMD27dsxdOhQbNu2DXfddRf27t2LjIyMXp2j/dZbb2HDhg146623EB4eLjfwP/30EzQaTY+vm69f3uvB9x6UP8DOB0szl3q7hPNSfn4+0tPTvV0G+QFmhUQwJySKWSERzAl1p6d+THiP9gMPPACn0yk3uP369cMdd9yB77//3u0iJ0+eLDfskydPhslkwv79+3s1/qqrrgIAzJw5Ew8//DBmzJiB5ORkAMC0adOQldXzZFb79+/H7t27UV1dLe9pTk1Nle9fv349Hn30UWRkZAAA7rzzTmzevBnr16/H2rVrhesFgNGjR2PTpk2IjY1FYGAgTCYTcnJyhJpsfxAdEo1I3fkzGRoREREREZEo4UZ77969yMvLw/DhwwGcOsSgr6a8j42Ndfk5NDQUVqtVeObumJgYl7Gd3SZyze+ioiIYjUa5yf6tsrIypKWludyWnp6OX375RajODu3t7Rg/fjyuuuoqnDx5EjqdDjt27MDEiRPx1Vdf4dJLL+3V8nzRwvELoVKpvF0G+bjExERvl0B+glkhEcwJiWJWSARzQu4QngwtODjY5Wen0ymf0+EpYWFh8jkUHU4//6ivJScno66uTj6P4rcSEhJczp8AgIKCgl5vhHV1dSgsLMSDD546vFqpVOKWW25BWlpaj4e3+4uO806IusOckChmhUQwJySKWSERzAm5Q7jRHjp0KN544w20t7cjPz8f999/PzIzMz1YGjB8+HBYrVa8/fbbaG9vR25uLt555x2PrW/kyJEYNWoUZs2aJR9vX1RUJO+xzsrKwvLly5GXl4e2tjZs3boVO3fuFDos/XT9+vXDxRdfjDVr1sBisaC9vR0ffvghDh8+jBEjRvT58/KGrr6sIDodc0KimBUSwZyQKGaFRDAn5A7hRjs7OxvffPMNqqqqMHr0aAQEBGDZsmWerA2pqalYvXo1Fi5cCIPBgHXr1mHWrFkeW59CocAHH3yAmJgYXHnllQgLC8PEiRPlWfMWLFiAefPm4fbbb4fJZMKyZcvw3nvvYeTIkb1e1wcffIDa2lqkp6fDYDDgsccew6pVq3DNNdf09dMiIiIiIiKic0ho1nGn04nFixd7vLEmcb4+63hJSQmSkpK8XQb5OOaERDErJII5IVHMColgTqg7PfVjwpf3uvzyy/Gf//ynzwoj9/h6o01ERERERHS+6qkfEz50fOLEifjb3/6GiooKWCwW+Z+/0Wq1nf6Lj4/v0/UMHjy4y3V1XOz+fObJSevo/MGckChmhUQwJySKWSERzAm5Q/jyXv/7v/8LAPjLX/4ChUIBSZKgUCjgdDo9VpwnNDY2npP1HD58+Jysx1dxlkYSwZyQKGaFRDAnJIpZIRHMCblDuNH29KW8iIiIiIiIiM4HwoeOE/WGXq/3dgnkB5gTEsWskAjmhEQxKySCOSF3CO/RDggIgEKhOON2fzt0nM6N8PBwb5dAfoA5IVHMColgTkgUs0IimBNyh/AebavVKk+AVl1djeeeew5///vfPVkb+bGCggJvl0B+gDkhUcwKiWBOSBSzQiKYE3KHcKMdGhoq/wsPD8eCBQuwbds2T9ZGRERERERE5HfO+hztI0eOoLa2ti9rISIiIiIiIvJ7wudoG41G+Rxtp9MJSZLw4osveqww8m+xsbHeLoH8AHNCopgVEsGckChmhUQwJ+QO4UY7Ly/v/w1SKhEdHY3AwEBP1ETnAUmSvF0C+QHmhEQxKySCOSFRzAqJYE7IHcKHjm/atAlJSUlISkpCXFwcAgMD8fTTT3uyNvJjlZWV3i6B/ABzQqKYFRLBnJAoZoVEMCfkDuFG+7333hO6jYiIiIiIiOhC1uOh4x9//DE++ugjlJeXY8GCBfLtDQ0NHi2M/Ftn11wn+i3mhEQxKySCOSFRzAqJYE7IHT022sHBwTAYDAgICIBer5dvT0hIwF/+8hePFkf+Ky0tzdslkB9gTkgUs0IimBMSxayQCOaE3KGQBM/yP3jwIIYNG+bpekhQfHw8jh8/7u0yulRTU4PIyEhvl0E+jjkhUcwKiWBOSBSzQiKYE+pOT/2Y8Kzjw4YNw3/+8x/k5eXBbrfLtz/44IPuVUjnJYvFwl9M1CPmhEQxKySCOSFRzAqJYE7IHcKN9jPPPINt27ahtLQUY8eOxaeffoqrr76ajTYRERERERHRaYRnHX/rrbewZ88exMfH491338W+ffsQECA8/IKQm5sLhUKBtrY2b5fidaGhod4ugfwAc0KimBUSwZyQKGaFRDAn5A7hPdrBwcEIDg5Ge3s7JEnCgAEDUFBQ4MnaLhh/+MMf8P777+PTTz/FNddc4+1y+sSOyh3AeXTpwayMLG+XcF6KiYnxdgnkJ5gVEsGckChmhUQwJ+QO4UZbo9GgtbUVl156KRYuXIj4+Hg4nU5P1nZBeP3119Hc3OztMvrcodJDMBgM3i6DfFxxcTGSk5O9XQb5AWaFRDAnJIpZIRHMCblD+Njvl19+GS0tLXj++edhsVjw7bffYtOmTW4XkJmZiYceegjTpk2DXq9HQkICXn75ZQDAxo0bER8f7/L4pUuXYsyYMS7j58+fjzvuuAM6nQ7x8fHYsmULfvrpJ1x55ZUICwvD5ZdfjqNHjwrVYzabMXfuXKSkpCAsLAwDBw7Exx9/DABwOp147rnncNFFF0Gv12PkyJHYtWvXWT/348ePY8mSJdiwYcNZL8NXtbe3e7sE8gM8zYJEMSskgjkhUcwKiWBOyB3Ce7QvueSSUwOUyj5vDDdu3IicnBy88cYbyMnJwZQpU3DttdcKj9+0aRO2b9+OzZs3Y9WqVfjTn/6EzMxMbN68GbGxsZg6dSoefPBBuWHuiiRJuPXWW6HVavHVV18hISEBRUVF8izrL7zwAlauXInt27dj6NCh2LZtG2655Rbs3bsXGRkZvXrOkiRh9uzZWLJkCRITE3t8fHZ2NrKzs+WfrVYr8vPzAZw6rL9jevmOWk0mE/R6PYqKiuQx8fHxaGlpQU1NDQAgMDAQKSkpqKqqQmNjIwAgLCwMUVFRKCwslJvlqKgoKJVKlJeXy8tKSUlBfX096urqXGooKyuDw+FAc3MzNBoN1Go16uvr5XE6nQ5OpxNNTU0AgICAABgMBlitVrS2tgIA1Go1QkNDYTab5XFarRYKhQJWq1W+zWg0wmazyc9ZqVRCp9OhoaFBPtpCo9FApVKhoaHBpYa2tjb5SAKRGvLz8xETEwOFQoGKigp5WWlpaaitrZWXHxISgtjYWJSUlMjLCg8PR0hICEpLS+VxiYmJaG5uRm1tLQAgKCgISUlJqKiokOvS6/UIDw93OUUjNjYWkiShsvLUcfkKhQJpaWmoqamBxWIBcOp8opiYGBQXF8sfEBEREQgODkZZWZm8rKSkJDQ2NuLkyZMAAJVKhcTERJSXl8NmswEADAYDTCYTCgsL5XFxcXFwOp2oqqrqVQ2RkZFQq9UuNTidTpjNZvl1VqvVSEhIcMmy0WiEwWBwyXJcXBza2tpQXV0tv4epqamorq6WM6LVahEdHY2ioiI5D5GRkVCpVC6XYkhJSUFDQ4Ncgy9uTx016HQ6FBcXy+MSEhLgcDjkGpRKJZKTk1FZWSlvYzqdDpGRkSgoKEDH1Ryjo6MRGBjoUkNqairMZrO8vWo0GsTFxaG0tBQtLS0AgH79+kGr1aKkpMSlBrvdjhMnTvSqht5uTx3bCrenrren5ORkWCyWC3p7qq2t5fb0/+PnU/c1mM1m+e8obk/8fOpqe+rYDrg98fOps+2pJ8LX0f7xxx8xbdo01NfX4/jx49i/fz/efvtt/OMf/xAZ3qXMzEykpqbi1VdflW+LiIjA6tWrYbPZsGTJEpcXaenSpfjss8+we/dueXxaWhpeeeUVAEBDQwMMBgPeeustTJ06FQDw7rvvIisrS/4l0ZXvv/8el19+OaqrqxEREXHG/QMGDMDcuXPx0EMPybfdcsstiImJwdq1a5Gbm4tx48ahtbUVSmX332G89NJLyMnJwaeffgrgVHh7c462r19H+/GPHkdwcLC3y+gzSzOXeruE81JDQwP0er23yyA/wKyQCOaERDErJII5oe702XW0H3zwQaxduxbz588HAGRkZOCuu+5yu9EGTn1rc7rQ0FBYrdYem9UOp09U0DE74G9vO31PaFeKiopgNBo7bbIBoKysDGlpaS63paen45dffhGqs0NBQQGeeuop7N27t1fj/EmSKQlByiBvl0E+7nz6MoY8i1khEcwJiWJWSARzQu4QbrQbGxtdzo1WKBRQqVQeKapDWFiYfGhHh9MPi+prycnJqKurQ21tLcLDw8+4PyEh4YyZ1gsKCoQO/T7dN998g5MnT2LEiBEut0+ePBl33HEH1q9f3/vifcy1pmuRnp7u7TLIx5WVlTEnJIRZIRHMCYliVkgEc0LuEJ4MTalUorW1FQqFAsCp4AUGBnqsMAAYPnw4rFYr3n77bbS3tyM3NxfvvPOOx9Y3cuRIjBo1CrNmzZIPAygqKpL3WGdlZWH58uXIy8tDW1sbtm7dip07dyIrq3eXfpoyZQoKCwuRl5cn/wOAdevW4e9//3ufPiciIiIiIiI6t4Qb7QceeAC33norTpw4gSVLluD3v/89Fi1a5MnakJqaitWrV2PhwoUwGAxYt24dZs2a5bH1KRQKfPDBB4iJiZFnLJ84caJ8Mv+CBQswb9483H777TCZTFi2bBnee+89jBw5slfrCQkJQXx8vMs/4NQkCiaTqc+fFxEREREREZ07PU6Gdu+998qHMj/zzDNoaGiAJEmYNGmSy6HkdG75+mRora2tCAriOdrUPeaERDErJII5IVHMColgTqg7bk+G9v3338v/37ZtGw4cONA3ldF5rbGxEUaj0dtlkI9jTkgUs0IimBMSxayQCOaE3CF86DgACF4JzKdptdpO/3Ucvt1XBg8e3OW6Tr+m8/mq41p9RN1hTkgUs0IimBMSxayQCOaE3NHjHm2bzYaffvoJkiTBbrfL/+8wdOhQjxbY1xobG8/Jeg4fPnxO1kNERERERES+RajRnjRpkvzz6f9XKBQoLCz0TGXk1zx96Tc6PzAnJIpZIRHMCYliVkgEc0Lu6HEyNPJNvj4ZGhERERER0fmqp36sV+doE4kqLy/3dgnkB5gTEsWskAjmhEQxKySCOSF3sNEmj7DZbN4ugfwAc0KimBUSwZyQKGaFRDAn5A422kRERERERER9iI02eYTBYPB2CeQHmBMSxayQCOaERDErJII5IXew0SaPMJlM3i6B/ABzQqKYFRLBnJAoZoVEMCfkDjba5BG87BuJYE5IFLNCIpgTEsWskAjmhNzBRpuIiIiIiIioD7HRJiIiIiIiIupDbLTJI+Li4rxdAvkB5oREMSskgjkhUcwKiWBOyB1stMkjnE6nt0sgP8CckChmhUQwJySKWSERzAm5g402eURVVZW3SyA/wJyQKGaFRDAnJIpZIRHMCbmDjTYRERERERFRH2KjTR6hUCi8XQL5AeaERDErJII5IVHMColgTsgdCkmSJG8XQb0XHx+P48ePe7sMIiIiIiKiC05P/ZjyHNbi93JzczFu3Di0trZC+f+1d+/RTZ1nusAf2cLyRZZl4Zssy5YvlIALBNeZFoaASC8UumYmLYRCQihuHHpI2pRhYNrxeNahk55O6TEmHKBDTDshQ1vCJTj4FLxo6MRNPA1p4uLMwKJMbMsXfJEt5IuELduSdf7geA8ixv4cSd4SPL+1WAtL/vZ+ZZ5P4vXe+9tK/ugmU15TDo1GI3cZkuKCYrlLoAl0d3cjJSVF7jIoDDArJII5IVHMColgTsgf7BaD5LXXXsOhQ4fw4YcfwuFwTNicKxQKREdHIzIyUnrs3XffxYIFC2a63IBrtjdDB53cZVCIGxgY4AcYCWFWSARzQqKYFRLBnJA/2GgHSWJiIp577jkMDQ3hmWeeuef3/d//+3/xhS98YQYrIyIiIiIiomAKeqNtNpuxaNEi9PT04Ny5c9BoNCgpKcG2bdtw9OhRlJaW+pzbvnv3bly8eBG1tbXS+AULFqC7uxvV1dXQaDQoKytDfn4+tm7diitXrmDevHk4duwY5s6dO2U9drsdpaWlqK6uhs1mg8FgwP79+7Fq1Sp4PB6Ul5fjyJEjsFqtmDNnDl588UWsXr162q971apVAG6fbh4I5eXlKC8vl752OBxoaGgAAERHR0vXCLhcLgCATqdDQkICLBaLNCYjIwMjIyPo7u4GAERGRiI7OxtdXV1wOp0AgPj4eKSmpqKpqQljY2MAgNTUVCiVSrS3t0vbys7ORl9fH3p7e31qaGtrw/DwMIaHhzE0NASVSoW+vj5pnEajgcfjwa1btwAAERER0Gq10lF/AFCpVIiLi4PdbpfGqdVqKBQKOBwO6bHExEQMDQ1Jr1mpVEKj0aC/v1+672FMTAyioqKknxUAGI1GuFwu9PT0SONMJhM6OzulujQaDVJSUtDY2IjxZQz0ej0UCgU6OjqkbeXm5sJms6G/vx8AEBsbi/T0dLS0tEivJykpCbGxsWhtbZXGZWZmYnBwEDabDQAwa9YsZGVloaOjA4ODgwCAhIQEJCUlobGxURqXnp4Or9eLzs5OALfPisjNzUV3dzcGBgYAAHFxcdDr9Whubobb7QYAJCcnIzo6Gm1tbdK2srKy4HQ6cfPmTQBAVFQUMjMz0d7ejqGhIQCAVquFTqdDU1OTNM5gMMDj8Ui3vBCtISUlBSqVyqeG6Oho2O126d9apVLBaDT6ZDkxMRFardYnywaDAW63G1arFcDtHOXk5MBqtUoZUavVSEtLg8VikfKQkpKCqKgon/ec7Oxs9Pf3SzWE4nwar0Gj0aC5uVkaZzQaMTw8LNUgmuW0tDRERkb61JCTkwO73S7N15iYGBgMBrS2tmJkZAQAMHv2bKjVarS0tPjUMBPzaWBgAA0NDZxPk8wnk8mEgYGBB3o+DQwMwOPxcD6Bn09T1XDr1i3p/wacT/x8utd8Gv/s4Xzi59NE82kqQV8MzWw24/Lly6isrITZbEZlZSXWr1+P69evo7a2VqjRrq+vR1VVFZYtW4YDBw6gpKQEZrMZhw4dQnp6OjZu3Ain04kLFy5MWovX68WKFSugVqtx+PBhGI1GWCwWuFwuzJ8/H3v37sW+fftQVVWFhQsX4vTp09i8eTMuXbqEgoKCT3SN9mRjFAoFUlNTMTo6iqysLGzbtg3PPvus0HZDfTG03TW75S7Bx27zbrlLICIiIiKi+0RILIa2du1aPPbYY9LfdTod6urqpjV++fLlAIAtW7Zg+/bt2LRpE0wmEwDgySefRHHx1Itd1dXVoba2FlarFcnJyQBu/5ZsXEVFBXbt2oWCggIAwIYNG3D8+HFUVFTg8OHDwvWKunjxIpYuXYrIyEhcvHgRTz31FNxuN7Zt2xbwfc206JFoJCUlyV0Ghbjm5mZpHhNNhlkhEcwJiWJWSARzQv6YkUY7PT3d5+u4uDg4HA7ho8J6vd5n7ESP3XlK8b1YLBYkJiZKTfbd2trakJub6/NYXl4erl27JlTndH3+85+X/r5mzRp897vfxbFjx+6LRntdzjrk5eXJXQaFuPHTjIimwqyQCOaERDErJII5IX9EyLnz+Ph46RqJcXdeXxRoJpMJvb290nUSdzMajT7XRwBAY2MjMjMzg1bTnSIiIsDbmhMREREREYU3WRvtxYsXw+Fw4MSJExgbG0NNTQ1OnToVtP0VFhZi6dKlKCoqks6nt1gs0hHr4uJilJWVob6+Hm63GydPnsT58+eFTku/m8fjgcvlkhZpGB4ehsvlkhac+OMf/4i6ujqMjIzA7XbjN7/5DV566SVs3LgxQK9WXrwVAolgTkgUs0IimBMSxayQCOaE/CFro52Tk4ODBw9i586d0Gq1ePnll1FUVBS0/SkUCpw9exZ6vR5LlixBfHw81qxZI62Kt2PHDjz//PNYt24ddDod9uzZgzNnzqCwsHDa+zp27BhiYmKk1cfVajViYmLw9ttvAwDa29uxadMm6HQ6JCUlYdeuXfjRj36EF154IXAvWEYqlUruEigMMCckilkhEcwJiWJWSARzQv4I+qrjFByhvup4Q0MDr9GmKTEnJIpZIRHMCYliVkgEc0KTmaofk/WINhEREREREdH9ZkZWHZ9JarV6wse1Wm1AjwDn5+ejpaVlwufa29uRkJAQsH0RERERERFR+OCp42Eq1E8dd7vdwrdvowcXc0KimBUSwZyQKGaFRDAnNBmeOk6yGBgYkLsECgPMCYliVkgEc0KimBUSwZyQP9hoU1DY7Xa5S6AwwJyQKGaFRDAnJIpZIRHMCfmDjTYRERERERFRALHRpqDgfQdJBHNCopgVEsGckChmhUQwJ+QPLoYWpkJ9MTQiIiIiIqL7FRdDI1nwlwAkgjkhUcwKiWBOSBSzQiKYE/IHG20KCpfLJXcJFAaYExLFrJAI5oREMSskgjkhf7DRJiIiIiIiIgogNtoUFImJiXKXQGGAOSFRzAqJYE5IFLNCIpgT8gcbbQoKrVYrdwkUBpgTEsWskAjmhEQxKySCOSF/sNGmoLBYLHKXQGGAOSFRzAqJYE5IFLNCIpgT8gcbbSIiIiIiIqIAYqNNREREREREFEBstCkoDAaD3CVQGGBOSBSzQiKYExLFrJAI5oT8wUabgsLtdstdAoUB5oREMSskgjkhUcwKiWBOyB9KuQsIJzU1NVi5ciVGR0ehVPJHN5kD/34AKSkpsu2/uKBYtn2TOKvVivj4eLnLoDDArJAI5oREMSskgjkhf7BbDJLXXnsNhw4dwocffgiHw/Gx5vyXv/wlvvWtb/mMcblcyM/Px4cffjjT5QZc12AXRgZG5C6DiIiIiIhoxvHU8SBJTEzEc889h5deemnC55966ik4nU7pT29vL5KSkvD000/PbKFBolAo5C6BwkBEBN+CSAyzQiKYExLFrJAI5oT8EfQj2mazGYsWLUJPTw/OnTsHjUaDkpISbNu2DUePHkVpaSlu3Lghff/u3btx8eJF1NbWSuMXLFiA7u5uVFdXQ6PRoKysDPn5+di6dSuuXLmCefPm4dixY5g7d+6U9djtdpSWlqK6uho2mw0GgwH79+/HqlWr4PF4UF5ejiNHjsBqtWLOnDl48cUXsXr16mm/7lWrVgG4fbq5iNdffx0DAwP45je/OeHz5eXlKC8vl752OBxoaGgAAERHRyMjIwM3btyAy+UCAOh0OiQkJPjc/y8jIwMjIyPo7u4GAERGRiI7OxtdXV1wOp0AgPj4eKSmpqKpqQljY2MAgNTUVCiVSrS3t0vbys7ORl9fH3p7e31qaGtrw/DwMLxeL4aGhqBSqdDX1yeN02g08Hg8uHXrFoDbb2BarVY66g8AKpUKcXFxsNvt0ji1Wg2FQgGHwyE9lpiYiKGhIek1K5VKaDQa9Pf3Sz+b2bNnQ61Wo6WlRRpnNBrhcrnQ09MjjTOZTOjs7JTq0mg0SElJQWNjI7xeLwBAr9dDoVCgo6ND2lZubi5sNhv6+/sBALGxsUhPT0dLS4v0epKSkhAbG4vW1lZpXGZmJgYHB2Gz2QAAs2bNQlZWFjo6OjA4OAgASEhIQFJSEhobG6Vx6enp8Hq96OzsBHD7Fxq5ubno7u7GwMAAACAuLg56vR7Nzc3StUXJycmIjo5GW1ubtK2srCw4nU7cvHkTABAVFYXMzEy0t7djaGgIAKDVaqHT6dDU1CSNMxgM8Hg86OrqmlYNKSkpUKlUPjWYTCbY7Xbp31qlUsFoNPpkOTExEVqt1ifLBoMBbrcbVqsVwO0c5eTkwGq1ShlRq9VIS0uDxWKBx+ORaoiKivJ5z8nOzkZ/f79UQyjOp/EaNBoNmpubpXFGoxHDw8NSDaJZTktLQ2RkpE8NOTk5sNvt0nyNiYmBwWBAa2srRkZun50i53waGxtDQ0MD59MU82lgYOCBn08ej4fzCfx8mqqGiIgI6f8KnE/8fJpsPjU0NHA+TaOGB2k+TUXhHU9VkJjNZly+fBmVlZUwm82orKzE+vXrcf36ddTW1go12vX19aiqqsKyZctw4MABlJSUwGw249ChQ0hPT8fGjRvhdDpx4cKFSWvxer1YsWIF1Go1Dh8+DKPRCIvFApfLhfnz52Pv3r3Yt28fqqqqsHDhQpw+fRqbN2/GpUuXUFBQ8Imu0RYds3z5cuTm5uKVV14R2u54MELVzl/vhFqtlm3/u827Zds3ibNarUhNTZW7DAoDzAqJYE5IFLNCIpgTmsxU/diMXKO9du1aPPbYY9LfdTod6urqpjV++fLlAIAtW7Zg+/bt2LRpE0wmEwDgySefRHHx1Itf1dXVoba2FlarFcnJyQBu/5ZsXEVFBXbt2oWCggIAwIYNG3D8+HFUVFTg8OHDwvVO15UrV/DOO+9g7969QdvHTNMpdUjRyLcYGoUHh8PBDzASwqyQCOaERDErJII5IX/MSKOdnp7u83VcXBwcDofwUWG9Xu8zdqLH7jyl+F4sFgsSExOlJvtubW1tyM3N9XksLy8P165dE6rzk/rpT3+KwsJCPPLII0Hdz0xan7ceeXl5cpdBREREREQ042S9wj8+Pl66RmLcndcXBZrJZEJvb690ncTdjEajz/URANDY2IjMzMyg1eRwOPCLX/wCzz33XND2IQc5Txun8MGckChmhUQwJySKWSERzAn5Q9ZGe/HixXA4HDhx4gTGxsZQU1ODU6dOBW1/hYWFWLp0KYqKiqTz6S0Wi3TEuri4GGVlZaivr4fb7cbJkydx/vx5odPS7+bxeOByuaRFGoaHh+FyuaQFJ8YdO3YMs2bNwoYNG/x8daElLS1N7hIoDDAnJIpZIRHMCYliVkgEc0L+kLXRzsnJwcGDB7Fz505otVq8/PLLKCoqCtr+FAoFzp49C71ejyVLliA+Ph5r1qyRVsXbsWMHnn/+eaxbtw46nQ579uzBmTNnUFhYOO19HTt2DDExMdLq42q1GjExMXj77bd9vu+f//mfsWXLFsTExPj/AkPInav1Ed0Lc0KimBUSwZyQKGaFRDAn5I+grzpOwRHqq443NDTwGm2aEnNCopgVEsGckChmhUQwJzSZqfox3oWdiIiIiIiIKIBmZNXxmXSvRQu0Wm1AjwDn5+ejpaVlwufa29uRkJAQsH2Fo5QU3tqLpsackChmhUQwJySKWSERzAn5475rtJ1O54zs5+rVqzOyn3AVFRUldwkUBpgTEsWskAjmhEQxKySCOSF/8NRxCopQvn6cQgdzQqKYFRLBnJAoZoVEMCfkDzbaRERERERERAHERpuIiIiIiIgogHh7rzAV6rf38ng8iIyMlLsMCnHMCYliVkgEc0KimBUSwZzQZHh7L5JFf3+/3CVQGGBOSBSzQiKYExLFrJAI5oT8wUabgsJut8tdAoUB5oREMSskgjkhUcwKiWBOyB9stImIiIiIiIgCiI02BUV0dLTcJVAYYE5IFLNCIpgTEsWskAjmhPzBxdDCVKgvhkZERERERHS/4mJoJAv+EoBEMCckilkhEcwJiWJWSARzQv5go01B4XK55C6BwgBzQqKYFRLBnJAoZoVEMCfkDzbaRERERERERAHERpuCQqfTyV0ChQHmhEQxKySCOSFRzAqJYE7IH2y0KSgSEhLkLoHCAHNCopgVEsGckChmhUQwJ+QPNtoUFBaLRe4SKAwwJySKWSERzAmJYlZIBHNC/mCjTURERERERBRASrkLuJ/U1NRg5cqVGB0dhVL5YP9oTzacRMpAyozsq7igeEb2Q0REREREJOLB7gZl9P3vfx/nzp1DS0sL4uLiYDab8ZOf/ARGo1Hu0gJiUDmIGwO89yBNLiMjQ+4SKEwwKySCOSFRzAqJYE7IHzx1XCYKhQJHjx6FzWbDtWvXoFAo8Bd/8RdylxUwHo9H7hIoDIyMjMhdAoUJZoVEMCckilkhEcwJ+UP2I9pmsxmLFi1CT08Pzp07B41Gg5KSEmzbtg1Hjx5FaWkpbtz47yOju3fvxsWLF1FbWyuNX7BgAbq7u1FdXQ2NRoOysjLk5+dj69atuHLlCubNm4djx45h7ty5U9Zjt9tRWlqK6upq2Gw2GAwG7N+/H6tWrYLH40F5eTmOHDkCq9WKOXPm4MUXX8Tq1aun/br/6Z/+Sfp7VFQU/vZv/xaLFy9Gb28vEhMTP/b95eXlKC8vl752OBxoaGgAAERHRyMjIwM3btyAy+UCcPt2BAkJCT6LOGRkZGBkZATd3d0AgMjISGRnZ6OrqwtOpxMAEB8fj9TUVDQ1NWFsbAwAkJqaCqVSifb2dmlb2dnZ6OvrQ29vr08NbW1tGB4eRk9PD2bPng2VSoW+vj5pnEajgcfjwa1btwAAERER0Gq1cDgcGB0dBQCoVCrExcXBbrdL49RqNRQKBRwOh/RYYmIihoaGpJ9DTEwMDAYDWltbpTfG2bNnQ61Wo6WlRRpnNBrhcrnQ09MDAFAqlTCZTOjs7JTq0mg0SElJQWNjI7xeLwBAr9dDoVCgo6ND2lZubi5sNhv6+/sBALGxsUhPT0dLS4v0epKSkhAbG4vW1lZpXGZmJgYHB2Gz2QAAs2bNQlZWFjo6OjA4OAjg9kqXSUlJaGxslMalp6fD6/Wis7MTwO1f2OTm5qK7uxsDAwMAgLi4OOj1ejQ3N8PtdgMAkpOTER0djba2NmlbWVlZcDqduHnzJoDbOczMzER7ezuGhoYAAFqtFjqdDk1NTdI4g8EAj8eDrq6uadWQkpIClUrlU4PH44Hb7Zb+rVUqFYxGo0+WExMTodVqfbJsMBjgdrthtVoB3M5RTk4OrFarlBG1Wo20tDRYLBbpFz8pKSmIioryeU/Jzs5Gf3+/VEMozqfxGjQaDZqbm6VxRqMRw8PDUg2iWU5LS0NkZKRPDTk5ObDb7dJ8DbX51NnZiaSkJM6nSeaTyWTCwMDAAz2fbDYbHnnkEc4n8PNpqhr+9Kc/Sbdu4nzi59O95pPNZkNSUhLn0zRqeJDm01QU3vFUycRsNuPy5cuorKyE2WxGZWUl1q9fj+vXr6O2tlao0a6vr0dVVRWWLVuGAwcOoKSkBGazGYcOHUJ6ejo2btwIp9OJCxcuTFqL1+vFihUroFarcfjwYRiNRlgsFrhcLsyfPx979+7Fvn37UFVVhYULF+L06dPYvHkzLl26hIKCAr+u0f7JT36Cn/70pz5vUpMZD0aoeuHMCzN278Hd5t0zsh8KvIaGBuTl5cldBoUBZoVEMCckilkhEcwJTWaqfkz2I9oAsHbtWjz22GPS33U6Herq6qY1fvny5QCALVu2YPv27di0aRNMJhMA4Mknn0Rx8dQLZtXV1aG2thZWqxXJyckAbv8WbVxFRQV27dqFgoICAMCGDRtw/PhxVFRU4PDhw8L13u3ixYv4wQ9+gNdff/0TbyPUpKvTkaRJkrsMCnGRkZFyl0BhglkhEcwJiWJWSARzQv4IiUY7PT3d5+u4uDg4HA7ho8J6vd5n7ESP3XnK8b1YLBYkJiZKTfbd2trakJub6/NYXl4erl27JlTnRH79619j06ZN+MUvfoEvf/nLn3g7oeb7X/q+3CVQGMjOzpa7BAoTzAqJYE5IFLNCIpgT8kdIL4YWHx8vXUMx7s7rjwLNZDKht7dXuo7ibkaj0ef6CQBobGxEZmbmJ9rfL3/5Szz11FM4ceIEvvrVr36ibYQqkesWiJgTEsWskAjmhEQxKySCOSF/hHSjvXjxYjgcDpw4cQJjY2OoqanBqVOngra/wsJCLF26FEVFRdL59haLRTpiXVxcjLKyMtTX18PtduPkyZM4f/680Gnpdzt48CC+/e1v49e//jVWrVoV0NcRCsYX2yCaDHNCopgVEsGckChmhUQwJ+SPkG60c3JycPDgQezcuRNarRYvv/wyioqKgrY/hUKBs2fPQq/XY8mSJYiPj8eaNWukVfN27NiB559/HuvWrYNOp8OePXtw5swZFBYWTntf3/nOd+B0OrF69Wqo1WrpzzvvvBPol0VEREREREQzSPZVx+mTCfVVx61WK1JTU+Uug0Icc0KimBUSwZyQKGaFRDAnNJmp+jE22mEq1BttIiIiIiKi+9VU/VhInzoeDHeepn3nn4yMjIDuJz8//577Gr/Z/f3szhveE90Lc0KimBUSwZyQKGaFRDAn5I+QuL3XTJqpRQ2uXr06I/sJVWNjY3KXQGGAOSFRzAqJYE5IFLNCIpgT8scDd0SbiIiIiIiIKJjYaFNQcOEIEsGckChmhUQwJySKWSERzAn5g402BYVS+cBdlUCfAHNCopgVEsGckChmhUQwJ+QPNtoUFO3t7XKXQGGAOSFRzAqJYE5IFLNCIpgT8gcbbSIiIiIiIqIAYqNNREREREREFEAKr9frlbsImr6pbpAuN4/Hg8jISLnLoBDHnJAoZoVEMCckilkhEcwJTWaqfoxHtCko+vr65C6BwgBzQqKYFRLBnJAoZoVEMCfkDzbaFBS9vb1yl0BhgDkhUcwKiWBOSBSzQiKYE/IHG20iIiIiIiKiAGKjTUERHR0tdwkUBpgTEsWskAjmhEQxKySCOSF/cDG0MBXqi6ERERERERHdr7gYGsmira1N7hIoDDAnJIpZIRHMCYliVkgEc0L+YKNNQTE8PCx3CRQGmBMSxayQCOaERDErJII5IX+w0SYiIiIiIiIKIKXcBYSTmpoarFy5EqOjo1Aq+aObzLnOc4gbiAv6fooLioO+DwoenU4ndwkUJpgVEsGckChmhUQwJ+QPdotB8tprr+HQoUP48MMP4XA4JmzOf/nLX+LHP/4xWlpaEB8fjyeeeAJ79uyBSqWSqerAuTl6E70DvPcgTU6j0chdAoUJZoVEMCckilkhEcwJ+YOnjgdJYmIinnvuObz00ksTPv/hhx/i6aefRmlpKfr6+vD73/8eFy5cwA9+8IOZLTRI+vr65C6BwkBzc7PcJVCYYFZIBHNCopgVEsGckD+CfkTbbDZj0aJF6Onpwblz56DRaFBSUoJt27bh6NGjKC0t9VkWfffu3bh48SJqa2ul8QsWLEB3dzeqq6uh0WhQVlaG/Px8bN26FVeuXMG8efNw7NgxzJ07d8p67HY7SktLUV1dDZvNBoPBgP3792PVqlXweDwoLy/HkSNHYLVaMWfOHLz44otYvXr1tF/3qlWrANw+3XwiTU1NSEhIwNe//nUAQFZWFr7yla/g8uXLE35/eXk5ysvLpa8dDgcaGhoA3L7H3/jy8i6XC8DtU10SEhJgsVikMRkZGRgZGUF3dzcAIDIyEtnZ2ejq6oLT6QQAxMfHIzU1FU1NTRgbGwMApKamQqlUor29XdpWdnY2+vr60Nvb61NDW1sbhoeHMTg4iJiYGKhUKp+mW6PRwOPx4NatWwCAiIgIaLVa6ag/AKhUKsTFxcFut0vj1Go1FAoFHA6H9FhiYiJsNpu0/ZiYGBgMBrS2tmJkZAQAMHv2bKjVarS0tEjjjEYjXC4Xenp6AABKpRImkwmdnZ1SXRqNBikpKWhsbMT4HfD0ej0UCgU6OjqkbeXm5sJms6G/vx8AEBsbi/T0dLS0tEivJykpCbGxsWhtbZXGZWZmYnBwEDabDQAwa9YsZGVloaOjA4ODgwCAhIQEJCUlobGxURqXnp4Or9eLzs5OAIBCoUBubi66u7sxMDAAAIiLi4Ner0dzczPcbjcAIDk5GdHR0T6rZ2ZlZcHpdOLmzZsAgKioKGRmZqK9vR1DQ0MAAK1WC51Oh6amJmmcwWCAx+NBV1fXtGpISUmBSqXyqcHj8cBut0v/1iqVCkaj0SfLiYmJ0Gq1Plk2GAxwu92wWq0AbucoJycHVqtVyoharUZaWhosFgs8Ho9UQ1RUlM97TnZ2Nvr7+6UaQnE+jdeg0Wh8PvSNRiOGh4elGkSznJaWhsjISJ8acnJyYLfbQ3Y+jc8Vzqd7zyeTyYSBgYEHej7ZbDbOp/+Pn0+T12C326X/R3E+8fPpXvNpfB5wPvHzaaL5NJWg30fbbDbj8uXLqKyshNlsRmVlJdavX4/r16+jtrZWqNGur69HVVUVli1bhgMHDqCkpARmsxmHDh1Ceno6Nm7cCKfTiQsXLkxai9frxYoVK6BWq3H48GEYjUZYLBa4XC7Mnz8fe/fuxb59+1BVVYWFCxfi9OnT2Lx5My5duoSCgoJPdI32vcYMDg7CbDbju9/9LjZs2IDm5masWbMGO3fuxLPPPjvldkP9PtovnHlhRq5r2W3eHfR9UPA0NDQgLy9P7jIoDDArJII5IVHMColgTmgyU/VjM3KN9tq1a/HYY49Jf9fpdKirq5vW+OXLlwMAtmzZgu3bt2PTpk0wmUwAgCeffBLFxVMvilVXV4fa2lpYrVYkJycDuP1bsnEVFRXYtWsXCgoKAAAbNmzA8ePHUVFRgcOHDwvXKyI2NhbFxcX49re/jW984xvweDzYvHkzvvnNbwZ0P3KZlzEPs5Sz5C6DQpzRaJS7BAoTzAqJYE5IFLNCIpgT8seMNNrp6ek+X8fFxcHhcAgfFdbr9T5jJ3rszlOK78VisSAxMVFqsu/W1taG3Nxcn8fy8vJw7do1oTqn49VXX8X3vvc9nD17FsuWLYPVasWzzz6Lp556Cq+99lrA9zfTnnroKS4gQVMaHh6+Lxb/o+BjVkgEc0KimBUSwZyQP2RdDC0+Pl66RmLcndcXBZrJZEJvb690ncTdjEajz/URANDY2IjMzMyA1/LBBx9g+fLlWL58OSIiIqDX67F161acPXs24PuSw/h1DESTYU5IFLNCIpgTEsWskAjmhPwha6O9ePFiOBwOnDhxAmNjY6ipqcGpU6eCtr/CwkIsXboURUVF0vn0FotFOmJdXFyMsrIy1NfXw+124+TJkzh//rzQael383g8cLlc0iINw8PDcLlc0oITjz76KN5++238/ve/h9frRU9PD372s5/hM5/5TIBeLREREREREclB1kY7JycHBw8exM6dO6HVavHyyy+jqKgoaPtTKBQ4e/Ys9Ho9lixZgvj4eKxZs0ZaFW/Hjh14/vnnsW7dOuh0OuzZswdnzpxBYWHhtPd17NgxxMTESKuPq9VqxMTE4O233wYArF+/Hrt378Y3v/lNJCQk4NOf/jRUKhV++ctfBu4Fy0j0sgB6sDEnJIpZIRHMCYliVkgEc0L+CPqq4xQcob7qOBERERER0f1qqn5M1iPadP8av+8f0WSYExLFrJAI5oREMSskgjkhf9x350Oo1eoJH9dqtQE9Apyfn4+WlpYJn2tvb0dCQkLA9hWO7l7kjmgizAmJYlZIBHNCopgVEsGckD/uu0bb6XTOyH6uXr06I/shIiIiIiKi8MJTxykoeA9tEsGckChmhUQwJySKWSERzAn5g4uhhSkuhkZERERERCQPLoZGsmhsbJS7BAoDzAmJYlZIBHNCopgVEsGckD/YaFNQ8EQJEsGckChmhUQwJySKWSERzAn5g402ERERERERUQCx0aagSEtLk7sECgPMCYliVkgEc0KimBUSwZyQP9hoU1BERkbKXQKFAeaERDErJII5IVHMColgTsgfbLQpKNrb2+UugcIAc0KimBUSwZyQKGaFRDAn5A822kREREREREQBxEabiIiIiIiIKIAUXq5bH5amukG63MbGxhARwd/j0OSYExLFrJAI5oREMSskgjmhyUzVjzE5FBR2u13uEigMMCckilkhEcwJiWJWSARzQv5go01B0dfXJ3cJFAaYExLFrJAI5oREMSskgjkhf7DRJiIiIiIiIgogNtoUFDExMXKXQGGAOSFRzAqJYE5IFLNCIpgT8gcXQwtTob4YGhERERER0f1qqn5MOYO13PdqamqwcuVKjI6OQql8sH+0ey7uwWzd7IBus7igOKDbI/m1trYiMzNT7jIoDDArJII5IVHMColgTsgfD3Y3KKP8/Hy0tLRIX4+NjWFoaAhnzpzBV7/6VRkrC4z2gXYMKYfkLoNC3MjIiNwlUJhgVkgEc0KimBUSwZyQP3iNtkyuXr0Kp9Mp/fnxj3+M2bNnY/Xq1XKXRkRERERERH6Q/Yi22WzGokWL0NPTg3PnzkGj0aCkpATbtm3D0aNHUVpa6nPu++7du3Hx4kXU1tZK4xcsWIDu7m5UV1dDo9GgrKwM+fn52Lp1K65cuYJ58+bh2LFjmDt37pT12O12lJaWorq6GjabDQaDAfv378eqVavg8XhQXl6OI0eOwGq1Ys6cOXjxxRcD0hz/8z//M5555hlER0dP+Hx5eTnKy8ulrx0OBxoaGgAA0dHR0jUCLpcLAKDT6ZCQkACLxSKNycjIwMjICLq7uwEAkZGRyM7ORldXF5xOJwAgPj4eqampaGpqwtjYGAAgNTUVSqUS7e3t0rays7PR19eH3t5enxra2towPDyM0dFRDA0NQaVS+dwaQaPRwOPx4NatWwCAiIgIaLVaOBwOjI6OAgBUKhXi4uJ87l2oVqsxNDTkU0NOTg7sdru0/ZiYGBgMBrS2tkq/gZw9ezbUarXP2QNGoxEulws9PT0AAKVSCZPJhM7OTqkujUaDlJQUNDY2YnwZA71eD4VCgY6ODmlbubm5sNls6O/vBwDExsYiPT0dLS0t0utJSkpCbGwsWltbpXGZmZkYHByEzWYDAMyaNQtZWVno6OjA4OAgACAhIQFJSUlobGyUxqWnp8Pr9aKzsxMAoFAokJubi+7ubgwMDAAA4uLioNfr0dzcDLfbDQBITk5GdHQ02trapG1lZWXB6XTi5s2bAICoqChkZmaivb0dQ0O3z0bQarXQ6XRoamqSxhkMBng8HnR1dU2rhpSUFKhUKp8atFot7Ha79G+tUqlgNBp9spyYmAitVuuTZYPBALfbDavVCuB2jnJycmC1WuFwOKTMpKWlwWKxwOPxSDVERUX5vKdkZ2ejv79fqiEU59N4DRqNBs3NzdI4o9GI4eFhqQbRLKelpSEyMjKs5tPQ0BAaGho4nyaZTyaTCQMDAw/0fBoaGoLH4+F8Aj+fpqpheHhY+n8U5xM/n+41n8Y/ezif+Pk00XyaiuyLoZnNZly+fBmVlZUwm82orKzE+vXrcf36ddTW1go12vX19aiqqsKyZctw4MABlJSUwGw249ChQ0hPT8fGjRvhdDpx4cKFSWvxer1YsWIF1Go1Dh8+DKPRCIvFApfLhfnz52Pv3r3Yt28fqqqqsHDhQpw+fRqbN2/GpUuXUFBQ8Imv0f63f/s3fPGLX0RDQwOys7OFxoT6Ymj/8Nt/QGRkZEC3udu8O6DbI/mNjo5i1qxZcpdBYYBZIRHMCYliVkgEc0KTCYvF0NauXYvHHntM+rtOp0NdXd20xi9fvhwAsGXLFmzfvh2bNm2CyWQCADz55JMoLp56Ia26ujrU1tbCarUiOTkZwO3foo2rqKjArl27UFBQAADYsGEDjh8/joqKChw+fFi43rv99Kc/xZe//GXhJjscxIzGICUxRe4yKMS1tLQgLy9P7jIoDDArJII5IVHMColgTsgfIdFop6en+3wdFxcHh8MhfFRYr9f7jJ3osfHTCyZjsViQmJgoNdl3a2trQ25urs9jeXl5uHbtmlCdE+no6MDZs2fxxhtvfOJthKL1eev5xkRERERERA+kkF4MLT4+XrqGYtyd1x8FmslkQm9vr3Qdxd2MRqPP9RMA0NjY6Ney/xUVFTAajVwEjYiIiIiI6D4R0o324sWL4XA4cOLECYyNjaGmpganTp0K2v4KCwuxdOlSFBUVSefbWywW6Yh1cXExysrKUF9fD7fbjZMnT+L8+fNCp6VPxO1248iRI/jWt76FiIiQ/qeYNqPRKHcJFAaYExLFrJAI5oREMSskgjkhf4R0d5eTk4ODBw9i586d0Gq1ePnll1FUVBS0/SkUCpw9exZ6vR5LlixBfHw81qxZI62at2PHDjz//PNYt24ddDod9uzZgzNnzqCwsPAT7e/s2bO4efMmnnnmmUC+jJAwvnof0WSYExLFrJAI5oREMSskgjkhf8i+6jh9MqG+6nhDQwOv0aYpMSckilkhEcwJiWJWSARzQpOZqh8L6SPaREREREREROEmJFYdn0lqtXrCx7VabUCPEOfn56OlpWXC59rb25GQkBCwfYWi6dxHnB5czAmJYlZIBHNCopgVEsGckD946niYCvVTx4mIiIiIiO5XPHWcZNHZ2Sl3CRQGmBMSxayQCOaERDErJII5IX+w0aaguPv+50QTYU5IFLNCIpgTEsWskAjmhPzBRpuIiIiIiIgogNhoU1BoNBq5S6AwwJyQKGaFRDAnJIpZIRHMCfmDi6GFKS6GRkREREREJA8uhkayaGxslLsECgPMCYliVkgEc0KimBUSwZyQP9hoU1DwRAkSwZyQKGaFRDAnJIpZIRHMCfmDjTYRERERERFRALHRpqDQ6/Vyl0BhgDkhUcwKiWBOSBSzQiKYE/IHG20KCoVCIXcJFAaYExLFrJAI5oREMSskgjkhf7DRpqDo6OiQuwQKA8wJiWJWSARzQqKYFRLBnJA/2GgTERERERERBRAbbSIiIiIiIqIAUni5bn1YmuoG6XLzer28roWmxJyQKGaFRDAnJIpZIRHMCU1mqn6MR7QpKGw2m9wlUBhgTkgUs0IimBMSxayQCOaE/KGUu4BwUlNTg5UrV2J0dBRKJX90kzlSdwQpKSnTHldcUByEaihU9ff3Izk5We4yKAwwKySCOSFRzAqJYE7IH+wWg+S1117DoUOH8OGHH8LhcHysOW9qasLTTz+N69evY2RkBMnJydiyZQv+/u//HhER4X+iQddgF0YGRuQug4iIiIiIaMax0Q6SxMREPPfccxgaGsIzzzzzseeTk5PxL//yL8jLy0NkZCSamprwla98BVqtFt/5zndkqDiwZs2aJXcJFAZiY2PlLoHCBLNCIpgTEsWskAjmhPwR9EbbbDZj0aJF6Onpwblz56DRaFBSUoJt27bh6NGjKC0t9bmIfPfu3bh48SJqa2ul8QsWLEB3dzeqq6uh0WhQVlaG/Px8bN26FVeuXMG8efNw7NgxzJ07d8p67HY7SktLUV1dDZvNBoPBgP3792PVqlXweDwoLy/HkSNHYLVaMWfOHLz44otYvXr1tF/3qlWrANw+3Xwi8fHxPvUqFApERETg+vXrE35/eXk5ysvLpa8dDgcaGhoAANHR0dLF+C6XCwCg0+mQkJAAi8UijcnIyMDIyAi6u7sBAJGRkcjOzkZXVxecTqdUV2pqKpqamjA2NgYASE1NhVKpRHt7u7St7Oxs9PX1obe316eGtrY2DA8PY3R0FENDQ1CpVOjr65PGaTQaeDwe3Lp1CwAQEREBrVYrHfVvaGiARqNBSkoKGhsbMb5WX1paGiIjI31qyMnJgd1ul7YfExMDg8GA1tZWjIzcPpo+e/ZsqNVqtLS0SOOMRiNcLhd6enoAAEqlEiaTCZ2dnVJdE9Wg1+uhUCh87qmYm5sLm82G/v5+ALffkNPT09HS0oLR0VEAQFJSEmJjY9Ha2iqNy8zMxODgoHTtz6xZs5CVlYWOjg4MDg4CABISEpCUlITGxkZpXHp6OrxeLzo7OwHczk1ubi66u7sxMDAAAIiLi4Ner0dzczPcbjeA27/YiY6ORltbm7StrKwsOJ1O3Lx5EwAQFRWFzMxMtLe3Y2hoCACg1Wqh0+nQ1NQkjTMYDPB4POjq6ppWDSkpKVCpVD41mEwm2O122O12AIBKpYLRaPTJcmJiIrRarU+WDQYD3G43rFarlKOcnBxYrVY4HA4AgFqtRlpaGiwWCzwej1RDVFSUz3tOdnY2+vv7pRpCcT6N16DRaNDc3CyNMxqNGB4elmoQzXK4zqeGhgbOpynm08DAwAM/nzweD+cT+Pk0VQ0jIyPS/6M4n/j5NNl8amho4HyaRg0P0nyaStBXHTebzbh8+TIqKythNptRWVmJ9evX4/r166itrRVqtOvr61FVVYVly5bhwIEDKCkpgdlsxqFDh5Ceno6NGzfC6XTiwoULk9bi9XqxYsUKqNVqHD58GEajERaLBS6XC/Pnz8fevXuxb98+VFVVYeHChTh9+jQ2b96MS5cuoaCg4BNdoz3VmEcffRQffPABXC4XMjIy8Jvf/Abz5s2bcruhvur49je2Q6vVTnvcbvPugNdCoaulpQVZWVlyl0FhgFkhEcwJiWJWSARzQpOZqh+bkVPH165di8cee0z6u06nQ11d3bTGL1++HACwZcsWbN++HZs2bYLJZAIAPPnkkygunnoRrbq6OtTW1sJqtUoLG+Tk5EjPV1RUYNeuXSgoKAAAbNiwAcePH0dFRQUOHz4sXO90vPPOO/B4PHjvvffw61//+hMtIBaKUqJTkKK5P14LBc/4b4GJpsKskAjmhEQxKySCOSF/zEijnZ6e7vN1XFwcHA6H8FFhvV7vM3aix8ZPH5iMxWJBYmLiPVcPbGtrQ25urs9jeXl5uHbtmlCdn1RkZCSWLl2K2tpabN26Fa+//npQ9zcT1uetR15entxlEBERERERzThZl7eOj4+XrpEYd+f1RYFmMpnQ29t7z3viGY1Gn+sjAKCxsRGZmZlBq+lOo6Oj97xGO9wkJSXJXQKFAeaERDErJII5IVHMColgTsgfsjbaixcvhsPhwIkTJzA2NoaamhqcOnUqaPsrLCzE0qVLUVRUJJ1Pb7FYpCPWxcXFKCsrQ319PdxuN06ePInz588LnZZ+N4/HA5fLJS3SMDw8DJfLJS048eabb+L3v/89hoeH4Xa78dZbb2H//v1Ys2ZNgF6tvLhKI4lgTkgUs0IimBMSxayQCOaE/CFro52Tk4ODBw9i586d0Gq1ePnll1FUVBS0/SkUCpw9exZ6vR5LlixBfHw81qxZI62Kt2PHDjz//PNYt24ddDod9uzZgzNnzqCwsHDa+zp27BhiYmKk1cfVajViYmLw9ttvA7i9avj/+B//A7Nnz8bs2bPx/PPP44UXXsCPfvSjwL1gGd254iLRvTAnJIpZIRHMCYliVkgEc0L+CPqq4xQcob7qeENDA6/RpikxJySKWSERzAmJYlZIBHNCk5mqH5P1iDYRERERERHR/WZGVh2fSWq1esLHtVptQI8A5+fno6WlZcLn2tvbkZCQELB9haOZWkCOwhtzQqKYFRLBnJAoZoVEMCfkj/uu0XY6nTOyn6tXr87IfsLV4OAgoqKi5C6DQhxzQqKYFRLBnJAoZoVEMCfkD546TkFxr1uoEd2JOSFRzAqJYE5IFLNCIpgT8gcbbSIiIiIiIqIAYqNNQTFr1iy5S6AwwJyQKGaFRDAnJIpZIRHMCfmDt/cKU6F+ey8iIiIiIqL7FW/vRbLo6OiQuwQKA8wJiWJWSARzQqKYFRLBnJA/2GhTUAwODspdAoUB5oREMSskgjkhUcwKiWBOyB9stImIiIiIiIgCiI02BUVCQoLcJVAYYE5IFLNCIpgTEsWskAjmhPzBRpuCIikpSe4SKAwwJySKWSERzAmJYlZIBHNC/mCjTUHR2NgodwkUBpgTEsWskAjmhEQxKySCOSF/sNEmIiIiIiIiCiA22kREREREREQBxEabgiI9PV3uEigMMCckilkhEcwJiWJWSARzQv5go01B4fV65S6BwgBzQqKYFRLBnJAoZoVEMCfkDzbaFBSdnZ1yl0BhgDkhUcwKiWBOSBSzQiKYE/IHG20iIiIiIiKiAFLKXcD9pKamBitXrsTo6CiUygf7R3uy4SRSBlKEvre4oDjI1VCoUigUcpdAYYJZIRHMCYliVkgEc0L+eLC7QZm9++67+Pu//3u8//77iIyMxPz581FbW4uIiPA/0WAkegQ3Bm7IXQaFuNzcXLlLoDDBrJAI5oREMSskgjkhf4R/Rxem3n33XaxevRpbtmyB1WqFzWbDvn377pvfnN26dUvuEigMdHd3y10ChQlmhUQwJySKWSERzAn5Q/Yj2mazGYsWLUJPTw/OnTsHjUaDkpISbNu2DUePHkVpaSlu3PjvI6O7d+/GxYsXUVtbK41fsGABuru7UV1dDY1Gg7KyMuTn52Pr1q24cuUK5s2bh2PHjmHu3LlT1mO321FaWorq6mrYbDYYDAbs378fq1atgsfjQXl5OY4cOQKr1Yo5c+bgxRdfxOrVq6f9uv/2b/8WzzzzDDZv3iw99tnPfvae319eXo7y8nLpa4fDgYaGBgBAdHQ0MjIycOPGDbhcLgCATqdDQkICLBaLNCYjIwMjIyPSm0ZkZCSys7PR1dUFp9MJAIiPj0dqaiqampowNjYGAEhNTYVSqUR7e7u0rezsbPT19aG3t9enhra2NgwPD6O3txcRERFQqVTo6+uTxmk0Gng8HqkRHz9639nZKT2m0WiQkpKCxsZGabXHtLQ0REZG+tSQk5MDu90ubT8mJgYGgwGtra0YGRkBAMyePRtqtRotLS3SOKPRCJfLhZ6eHgCAUqmEyWSasga9Xg+FQoGOjg5pW7m5ubDZbOjv7wcAxMbGIj09HS0tLRgdHQUAJCUlITY2Fq2trdK4zMxMDA4OwmazAQBmzZqFrKwsdHR0YHBwEACQkJCApKQkNDY2SuPS09Ph9XqlxTkUCgVyc3PR3d2NgYEBAEBcXBz0ej2am5vhdrsBAMnJyYiOjkZbW5u0raysLDidTty8eRMAEBUVhczMTLS3t2NoaAgAoNVqodPp0NTUJI0zGAzweDzo6uqaVg0pKSlQqVQ+NXg8HiiVStjtdgCASqWC0Wj0yXJiYiK0Wq1Plg0GA9xuN6xWK4DbOcrJyYHVaoXD4QAAqNVqpKWlwWKxwOPxSDVERUX5vKdkZ2ejv79fqiEU59N4DRqNBs3NzdI4o9GI4eFhqQbRLIfjfOrs7MTAwADn0yTzyWQyYWBg4IGeTzabDbNnz+Z8Aj+fRGoYf4zziZ9P95pPNpsNAwMDnE/TqOFBmk9TUXhlXrfebDbj8uXLqKyshNlsRmVlJdavX4/r16+jtrZWqNGur69HVVUVli1bhgMHDqCkpARmsxmHDh1Ceno6Nm7cCKfTiQsXLkxai9frxYoVK6BWq3H48GEYjUZYLBa4XC7Mnz8fe/fuxb59+1BVVYWFCxfi9OnT2Lx5My5duoSCggLha7QHBwcRHx+Pv/mbv0FNTQ0aGxthMplQUlKCtWvXCv3cxoMRql448wJ0Op3Q9+427w5uMRSyGhoakJeXJ3cZFAaYFRLBnJAoZoVEMCc0man6MdmPaAPA2rVr8dhjj0l/1+l0qKurm9b45cuXAwC2bNmC7du3Y9OmTTCZTACAJ598EsXFUy+4VVdXh9raWlitViQnJwO4/Vu0cRUVFdi1axcKCgoAABs2bMDx48dRUVGBw4cPC9drt9sxNjaGV199Fb/+9a+xePFiVFVVYcOGDfjd736HJUuWCG8rVGUmZkKr0cpdBoW4uLg4uUugMMGskAjmhEQxKySCOSF/hESjnZ6e7vN1XFwcHA6H8Mrder3eZ+xEj42fXjAZi8WCxMREqcm+W1tb28cWRcjLy8O1a9eE6hwXHx8P4PYvBR555BEAwNe+9jWsXLkSb7zxxn3RaO9cuVPuEigM3DlPiSbDrJAI5oREMSskgjkhf4T0Ymjx8fEfW1TrzuuPAs1kMqG3t1e6juJuRqPR5/oJAGhsbERmZua09pOQkIDc3Nz7ZuGzidx5nQ7RvTAnJIpZIRHMCYliVkgEc0L+COlGe/HixXA4HDhx4gTGxsZQU1ODU6dOBW1/hYWFWLp0KYqKiqTz7S0Wi3TEuri4GGVlZaivr4fb7cbJkydx/vx5odPS7/ad73wHR48eRX19PcbGxlBVVYXf/e53+NrXvhbQ1ySX8QURiCbDnJAoZoVEMCckilkhEcwJ+SMkTh2/l5ycHBw8eBA7d+7Es88+i6985SsoKirCH/7wh6DsT6FQ4OzZs/i7v/s7LFmyBH19fcjIyMD+/fsxb9487NixAx6PB+vWrUN3dzfmzJmDM2fOoLCwcNr7+u53v4vBwUH8xV/8Bfr6+jBnzhycOHFi0pXHiYiIiIiIKPTJvuo4fTKhvup4f38/EhIS5C6DQhxzQqKYFRLBnJAoZoVEMCc0man6sZA+dZzCV3R0tNwlUBhgTkgUs0IimBMSxayQCOaE/PHANdpqtXrCPxkZGQHdT35+/j33NX6z+/vZnTepJ7oX5oREMSskgjkhUcwKiWBOyB8hfY12MDidzhnZz9WrV2dkP0RERERERBRaHrgj2kRERERERETBxMXQwlSoL4Y2OjqKWbNmyV0GhTjmhEQxKySCOSFRzAqJYE5oMlwMjWQxU6foU3hjTkgUs0IimBMSxayQCOaE/MFGm4Li5s2bcpdAYYA5IVHMColgTkgUs0IimBPyBxttIiIiIiIiogBio01BERUVJXcJFAaYExLFrJAI5oREMSskgjkhf3AxtDAV6ouhERERERER3a+4GBrJor29Xe4SKAwwJySKWSERzAmJYlZIBHNC/mCjTUExNDQkdwkUBpgTEsWskAjmhEQxKySCOSF/sNEmIiIiIiIiCiA22hQUWq1W7hIoDDAnJIpZIRHMCYliVkgEc0L+YKNNQaHT6eQugcIAc0KimBUSwZyQKGaFRDAn5A822hQUTU1NcpdAYYA5IVHMColgTkgUs0IimBPyBxttIiIiIiIiogBio01EREREREQUQEq5CwgnNTU1WLlyJUZHR6FU8kc3md/YfoOagRqfx4oLiuUphkKWwWCQuwQKE8wKiWBOSBSzQiKYE/IHj2gHyWuvvYZHH30UGo0GCoUCbrf7Y98zPDyMkpISZGVlIS4uDllZWfjXf/1XGaoNvBsDNz72h+huHo9H7hIoTDArJII5IVHMColgTsgfbLSDJDExEc899xxeeumle37PE088gffffx+//e1v4XQ68f777+Ozn/3szBUZRE6nU+4SKAx0dXXJXQKFCWaFRDAnJIpZIRHMCfkj6Oc/m81mLFq0CD09PTh37hw0Gg1KSkqwbds2HD16FKWlpbhx47+Pdu7evRsXL15EbW2tNH7BggXo7u5GdXU1NBoNysrKkJ+fj61bt+LKlSuYN28ejh07hrlz505Zj91uR2lpKaqrq2Gz2WAwGLB//36sWrUKHo8H5eXlOHLkCKxWK+bMmYMXX3wRq1evnvbrXrVqFYDbp5tP5Le//S3efPNNtLS0ICUlBQCQkpIi/f1u5eXlKC8vl752OBxoaGgAAERHRyMjIwM3btyAy+UCcPt2BAkJCbBYLNKYjIwMjIyMoLu7GwAQGRmJ7OxsdHV1SY1xfHw8UlNT0dTUhLGxMQBAamoqlEol2tvbpW1lZ2ejr68Pvb29PjW0tbVheHgYg4ODiImJgUqlQl9fHwCgoaEBRqMRw8PDUg1KpRImkwmdnZ24desWAECj0SAlJQWNjY3wer0AgLS0NERGRvrUkJOTA7vdLm0/JiYGBoMBra2tGBkZAQDMnj0barUaLS0t0jij0QiXy4Wenp5p1aDX66FQKNDR0SFtKzc3FzabDf39/QCA2NhYpKeno6WlBaOjowCApKQkxMbGorW1VRqXmZmJwcFB2Gw2AMCsWbOQlZWFjo4ODA4OAgASEhKQlJSExsZGaVx6ejq8Xi86OzsBAAqFArm5ueju7sbAwAAAIC4uDnq9Hs3NzdKZFMnJyYiOjkZbW5u0raysLDidTty8eRMAEBUVhczMTLS3t2NoaAjA7ftH6nQ6n1U3DQYDPB6P9OEjWkNKSgpUKpVPDR6PB3a7HXa7HQCgUqlgNBp9spyYmAitVuuTZYPBALfbDavVCgCIiIhATk4OrFYrHA4HAECtViMtLQ0Wi0X6jXRKSgqioqJ83nOys7PR398v1RCK82m8Bo1Gg+bmZmncgzSfxucK59O955PJZMLAwMADPZ9sNhvn0//Hz6fJa7Db7dL/ozif+Pl0r/k0Pg84n/j5NNF8morCO56qIDGbzbh8+TIqKythNptRWVmJ9evX4/r166itrRVqtOvr61FVVYVly5bhwIEDKCkpgdlsxqFDh5Ceno6NGzfC6XTiwoULk9bi9XqxYsUKqNVqHD58GEajERaLBS6XC/Pnz8fevXuxb98+VFVVYeHChTh9+jQ2b96MS5cuoaCg4BNdo32vMd///vdRVVWF1atX41e/+hWUSiW+8IUv4H//7/+NpKSkKbc7HoxQ9cKZFz5278Hd5t3yFEMhq7GxEbm5uXKXQWGAWSERzAmJYlZIBHNCk5mqH5uRU8fXrl2Lxx57DBEREVi7di10Oh3q6uqmNX758uWIiIjAli1bMDg4iE2bNsFkMiEqKgpPPvkk/vCHP0y5nbq6OtTW1uLVV19FZmYmFAoFcnJyMH/+fABARUUFdu3ahYKCAiiVSmzYsAGrV69GRUXFJ37t92Kz2XDt2jUMDw+joaEBH3zwAW7cuIGnn3464PuSw0LTQmRoMnz+EN2NH14kilkhEcwJiWJWSARzQv6YkaWz09PTfb6Oi4uDw+EQPiqs1+t9xk702PjpA5OxWCxITExEcnLyhM+3tbV9bELl5eXh2rVrQnVOx/giaT/5yU8QGxuLuLg4/OM//iP+/M//HIODg4iNjQ34PmfSX2b85T1Pgyca193dzZyQEGaFRDAnJIpZIRHMCflD1sXQ4uPjpWskxt15fVGgmUwm9Pb2StdJ3M1oNPpcHwHcPmUkMzMz4LUUFBRM+LhCoUCQz+afEePXbhBNhjkhUcwKiWBOSBSzQiKYE/KHrI324sWL4XA4cOLECYyNjaGmpganTp0K2v4KCwuxdOlSFBUVSefTWywW6Yh1cXExysrKUF9fD7fbjZMnT+L8+fMoLp7+/Z89Hg9cLpe0SMPw8DBcLpe04MRXv/pVGAwGlJSUwOVy4ebNm9i9ezfWrFkjHbUnIiIiIiKi8CNro52Tk4ODBw9i586d0Gq1ePnll1FUVBS0/SkUCpw9exZ6vR5LlixBfHw81qxZI62Kt2PHDjz//PNYt24ddDod9uzZgzNnzqCwsHDa+zp27BhiYmKk1cfVajViYmLw9ttvA7h9uvubb76Jq1evIikpCQsWLIDRaMSrr74auBcsI/6ygEQwJySKWSERzAmJYlZIBHNC/gj6quMUHKG+6jgREREREdH9KiRWHacHz533UiS6F+aERDErJII5IVHMColgTsgfM7Lq+ExSq9UTPq7VagN6BDg/Px8tLS0TPtfe3o6EhISA7Sscjd+0nmgyzAmJYlZIBHNCopgVEsGckD/uu0bb6XTOyH6uXr06I/shIiIiIiKi8MJTxykoeM9BEsGckChmhUQwJySKWSERzAn5g402BYVKpZK7BAoDzAmJYlZIBHNCopgVEsGckD/YaFNQjN8yjWgyzAmJYlZIBHNCopgVEsGckD/YaBMREREREREFEBttIiIiIiIiogBSeL1er9xF0PRNdYN0ubndbiiV992i9hRgzAmJYlZIBHNCopgVEsGc0GSm6sd4RJuCYmBgQO4SKAwwJySKWSERzAmJYlZIBHNC/mCjTUFht9vlLoHCAHNCopgVEsGckChmhUQwJ+QPNtpEREREREREAcRGm4KC9x0kEcwJiWJWSARzQqKYFRLBnJA/uBhamAr1xdCIiIiIiIjuV1wMjWTBXwKQCOaERDErJII5IVHMColgTsgfbLQpKFwul9wlUBhgTkgUs0IimBMSxayQCOaE/MFGm4iIiIiIiCiA2GhTUCQmJspdAoUB5oREMSskgjkhUcwKiWBOyB9stCkotFqt3CVQGGBOSBSzQiKYExLFrJAI5oT8wUZ7GmpqaqBQKOB2u+UuJeTteXMPfvbHn+Fnf/yZ3KVQCLNYLHKXQGGCWSERzAmJYlZIBHNC/mCjHSSvvfYaHn30UWg0mgmb86GhITzxxBOYM2cOIiIiUFpaKlOlwdE12IUbAzdwY4CrNRIRERER0YOFjXaQJCYm4rnnnsNLL7004fMKhQJLly5FRUUF/uzP/mxmiyMiIiIiIqKgUQZ7B2azGYsWLUJPTw/OnTsHjUaDkpISbNu2DUePHkVpaanPPep2796Nixcvora2Vhq/YMECdHd3o7q6GhqNBmVlZcjPz8fWrVtx5coVzJs3D8eOHcPcuXOnrMdut6O0tBTV1dWw2WwwGAzYv38/Vq1aBY/Hg/Lychw5cgRWqxVz5szBiy++iNWrV0/7da9atQrA7dPNJxIdHY2//uu/lv4+lfLycpSXl0tfOxwONDQ0SOPHb5g+fhsCnU6HhIQEn1NeMjIyMDIygu7ubgBAZGQksrOz0dXVBafTCQCIj49HamoqmpqaMDY2BgBITU2FUqlEe3u7tK3s7Gz09fWht7fXp4a2tjYMDw/D4/FgaGgIKpVKqhMAjEYjhoeHpRqUSiVMJhM6Oztx69YtAIBGo0FKSgoaGxvh9XoBAGlpaYiMjPSpIScnB3a7HX19fQCAmJgYGAwGtLa2YmRkBAAwe/ZsqNVqtLS0+NTgcrnQ09MzrRr0ej0UCgU6OjqkbeXm5sJms6G/vx8AEBsbi/T0dLS0tGB0dBQAkJSUhNjYWLS2tkrjMjMzMTg4CJvNBgCYNWsWsrKy0NHRgcHBQQBAQkICkpKS0NjYKI1LT0+H1+tFZ2cngNu/sMnNzUV3dzcGBgYAAHFxcdDr9WhubpbOpEhOTkZ0dDTa2tqkbWVlZcHpdOLmzZsAgKioKGRmZqK9vR1DQ0MAbl+bpNPp0NTUJI0zGAzweDzo6uqaVg0pKSlQqVQ+NaSmpsJut8NutwMAVCoVjEajT5YTExOh1Wp9smwwGOB2u2G1WgEAERERyMnJgdVqhcPhAACo1WqkpaXBYrHA4/FINURFRfm852RnZ6O/v1+qIRTn03gNGo0Gzc3N0rgHbT41NDRwPk0yn0wmEwYGBh7o+TQ6OgqPx8P5BH4+TVXD2NiY9P8Tzid+Pt1rPo1/9nA+8fNpovk0FYV3PFVBYjabcfnyZVRWVsJsNqOyshLr16/H9evXUVtbK9Ro19fXo6qqCsuWLcOBAwdQUlICs9mMQ4cOIT09HRs3boTT6cSFCxcmrcXr9WLFihVQq9U4fPgwjEYjLBYLXC4X5s+fj71792Lfvn2oqqrCwoULcfr0aWzevBmXLl1CQUEBampqsHLlSoyOjkKpFPsdhcgYs9mMZcuW4Yc//KHgTxVSMELV3134O6hUKgDAbvNueYuhkOVwOBAfHy93GRQGmBUSwZyQKGaFRDAnNJmp+rEZOXV87dq1eOyxxxAREYG1a9dCp9Ohrq5uWuOXL1+OiIgIbNmyBYODg9i0aRNMJhOioqLw5JNP4g9/+MOU26mrq0NtbS1effVVZGZmQqFQICcnB/PnzwcAVFRUYNeuXSgoKIBSqcSGDRuwevVqVFRUfOLX/qCK98YjQ5OBDE2G3KVQCBv/DSXRVJgVEsGckChmhUQwJ+SPoJ86Dtw+/eFOcXFxcDgcwkeF9Xq9z9iJHhs/fWAyFosFiYmJSE5OnvD5trY25Obm+jyWl5eHa9euCdVJ/2193nrk5eXJXQYREREREdGMk3UxtPj4eOkaiXF3Xl8UaCaTCb29vdJ1EnczGo0+10cAQGNjIzIzM4NW0/0qIoLr7NHUmBMSxayQCOaERDErJII5IX/Imp7FixfD4XDgxIkTGBsbQ01NDU6dOhW0/RUWFmLp0qUoKiqSzqe3WCzSEevi4mKUlZWhvr4ebrcbJ0+exPnz51FcXDztfXk8HrhcLmmRhuHhYbhcLmnBibsfu/v7w11OTo7cJVAYYE5IFLNCIpgTEsWskAjmhPwha6Odk5ODgwcPYufOndBqtXj55ZdRVFQUtP0pFAqcPXsWer0eS5YsQXx8PNasWSOtirdjxw48//zzWLduHXQ6Hfbs2YMzZ86gsLBw2vs6duwYYmJipNXH1Wo1YmJi8Pbbb0vfM3fuXMTExOCdd97Bj3/8Y8TExOBLX/pSYF6szHhNC4lgTkgUs0IimBMSxayQCOaE/BH0VccpOEJ91fGGhgZeo01TYk5IFLNCIpgTEsWskAjmhCYTEquOExERERERET0oZmTV8ZmkVqsnfFyr1Qb0CHB+fj5aWlomfK69vR0JCQkB21c4ute/A9GdmBMSxayQCOaERDErJII5IX/w1PEwFeqnjhMREREREd2veOo4ycJischdAoUB5oREMSskgjkhUcwKiWBOyB9stCkoPB6P3CVQGGBOSBSzQiKYExLFrJAI5oT8wUabiIiIiIiIKIDYaFNQpKSkyF0ChQHmhEQxKySCOSFRzAqJYE7IH2y0KSiioqLkLoHCAHNCopgVEsGckChmhUQwJ+QPNtoUFFwRnUQwJySKWSERzAmJYlZIBHNC/mCjTURERERERBRAbLSJiIiIiIiIAkjh9Xq9chdB0zfVDdLl5vF4EBkZKXcZFOKYExLFrJAI5oREMSskgjmhyUzVj/GINgVFf3+/3CVQGGBOSBSzQiKYExLFrJAI5oT8wUabgsJut8tdAoUB5oREMSskgjkhUcwKiWBOyB9stImIiIiIiIgCiI02BUV0dLTcJVAYYE5IFLNCIpgTEsWskAjmhPzBxdDCVKgvhkZERERERHS/4mJoJAv+EoBEMCckilkhEcwJiWJWSARzQv5go01B4XK55C6BwgBzQqKYFRLBnJAoZoVEMCfkDzbaRERERERERAHEa7TDlEqlQnJystxl3JPT6YRarZa7DApxzAmJYlZIBHNCopgVEsGc0GR6enowPDx8z+fZaFNQcLE2EsGckChmhUQwJySKWSERzAn5g6eOExEREREREQUQG20iIiIiIiKiAGKjTUGxY8cOuUugMMCckChmhUQwJySKWSERzAn5g9doExEREREREQUQj2gTERERERERBRAbbSIiIiIiIqIAYqNNAfXRRx9h6dKl+NSnPoVHHnkEV69elbskkskLL7wAk8kEhUKB+vp66fHJMsL8PHhcLhcef/xxfOpTn8KiRYvwxS9+EQ0NDQCA7u5ufPnLX8acOXPw6U9/Gm+//bY0brLn6P70pS99CQsXLsTDDz+MRx99FJcvXwbA9xS6t1deeQUKhQJvvPEGAL6nkC+TyYS5c+fi4YcfxsMPP4wTJ04A4HsKBZCXKIBWrlzpfeWVV7xer9d76tQpb2FhobwFkWx+97vfedva2rxZWVney5cvS49PlhHm58EzNDTkPXfunHdsbMzr9Xq9Bw4c8K5YscLr9Xq9RUVF3v/5P/+n1+v1ev/whz94DQaDd2RkZMrn6P7U29sr/f3MmTPehQsXer1evqfQxCwWi3fJkiXez33uc97Kykqv18v3FPJ19/9PxvE9hQKFjTYFjNVq9cbHx3tHR0e9Xq/XOzY25k1NTfV+9NFHMldGcrrzg2yyjDA/5PV6ve+//743KyvL6/V6vXFxcd7Ozk7puUceecT75ptvTvkc3f9eeeUV76JFi/ieQhPyeDzez3/+894PPvjAu2LFCqnR5nsK3WmiRpvvKRRIPHWcAqatrQ16vR5KpRIAoFAokJmZidbWVpkro1AxWUaYHwKA/fv346/+6q9w8+ZNjI6OIi0tTXrOZDKhtbV10ufo/rZ582YYjUb8wz/8A44dO8b3FJpQeXk5/vzP/xyf+cxnpMf4nkIT2bx5MxYsWIBnnnkGPT09fE+hgGKjTUREIeFHP/oRGhoa8E//9E9yl0Ih6l//9V/R1taGH/7wh/je974ndzkUgq5cuYLXX38dpaWlcpdCIe7tt9/Gf/zHf+CPf/wjkpKS8I1vfEPukug+w0abAsZoNKKzsxNutxsA4PV60draiszMTJkro1AxWUaYnwdbWVkZzpw5g+rqasTGxmL27NlQKpXo6uqSvqe5uRmZmZmTPkcPhm984xt46623kJGRwfcU8vHOO++gubkZc+bMgclkwqVLl7B161acPHmS7ynkY/zfd9asWdi+fTveeecd/j+FAoqNNgVMSkoKCgoK8Itf/AIA8PrrryMjIwN5eXkyV0ahYrKMMD8PrvLychw/fhxvvvkmtFqt9PgTTzyBw4cPAwDef/99tLe3Y8WKFVM+R/efvr4+dHR0SF+/8cYbmD17Nt9T6GO2bduGzs5ONDc3o7m5GZ/73OdQUVGBbdu28T2FJLdu3UJfX5/09fHjx7F48WK+p1BAKbxer1fuIuj+cf36dWzZsgU3b96ERqPBK6+8ggULFshdFsngW9/6Fs6dO4euri7Mnj0b8fHxaGhomDQjzM+D58aNGzAajcjJyUF8fDwAQKVS4b333oPVasXTTz8Ni8WCqKgoHDx4ECtXrgSASZ+j+09LSwueeOIJDA0NISIiAsnJySgrK8PDDz/M9xSalNlsxvbt2/H444/zPYUkTU1NWLt2LTweD7xeL3JycrB//36YTCa+p1DAsNEmIiIiIiIiCiCeOk5EREREREQUQGy0iYiIiIiIiAKIjTYRERERERFRALHRJiIiIiIiIgogNtpEREREREREAcRGm4iIiIiIiCiA2GgTERHd50ZHR/GDH/wADz30EPLz87F48WI8/vjjqK+v93vbhYWFqKmpAQAUFxfjrbfe8mt7b7zxBi5duuTz2D/+4z/i05/+NBYtWoSHHnoIu3btkp5TKBTo6+vz+X6TyfSx17ZixQrk5eXh7ruaKhQKLFiwAIsWLcKCBQtw6tQpv+onIiICAKXcBRAREVFwFRUVwel04t1330ViYiIA4OLFi7h+/Toefvhhn+/1eDyIjIz8RPv52c9+5m+peOONN/Dwww/jc5/7HADg9OnTqK6uxvvvv4+YmBi43W5cvXp1Wtv86KOP8NFHHyExMRG/+93vYDabfZ5/5513oNVq8cEHH2D58uVYuXIlkpKS/H4tRET04GKjTUREdB/76KOPUFlZiba2NqnJBoAvfOELAICjR4/i1VdfhU6nw3/913+hoqIC7777Lo4fP47R0VHMmjUL/+f//B8sWbIEAPD73/8ezz33HNxuNx555BG43W5pm2azGdu3b8fjjz8Oh8OBHTt24MMPP4TL5cLnPvc5HDx4EFFRUTCbzSgsLMR7772Hjo4OfPGLX8Thw4dx/vx5VFVV4c0338TRo0fx7W9/G06nEzqdDtHR0QAApVKJRYsWTetn8C//8i/YtGkT9Ho9fv7zn3+s0R5XWFgItVqN5uZmNtpEROQXNtpERET3scuXLyMvLw86ne6e3/Pee+/h8uXLmDt3LgAgLy8PO3bsAABcunQJW7ZswZ/+9CeMjIzg61//Ol555RV84QtfwG9+8xscPXp0wm3+zd/8DR599FEcOXIEXq8Xzz77LPbv3y+d9t3Y2Ii33noLo6OjmD9/Pt59912sWbMGf/mXf4mHH34Y27dvBwB0dXWhoqICOTk5ePTRR2E2m7Fx40bExMRI+3r00Ud9jsJ3dHRIf/d4PHj11Vfxb//2b0hKSsIPfvAD9Pf3IyEh4WM1X7x4EcPDw5gzZ47YD5eIiOge2GgTERE9QBobG7F27VoMDQ1h6dKlWLFiBZYuXSo12cDt5vx//a//hZs3b0KpVOL69esYGhrCRx99BKVSKR0N/9KXvoScnJwJ9/PGG2/g3XffRXl5OQBgaGjIpxn++te/DqVSCaVSiYcffhiNjY3SUfM7paWl4T//8z/x3nvv4d///d/x05/+FAcOHMB7772HqKgoAP996vc4k8kk/f38+fMwmUx46KGHANw+kv+rX/0K27Ztk75nvFFPTEzE2bNnJ2zCiYiIpoONNhER0X1s8eLFaGhoQG9vLxITE5Gbm4v6+nocPXoUb7zxBgBArVZL3z8yMoKvfe1reOutt/DII49gYGAACQkJGB4ennD7CoViwse9Xi9ef/11fOpTn5rw+fFTwQEgMjLS5xT0u0VGRmLp0qVYunQpXnjhBaSmpuLKlSsoKCiY6uXj5z//Of7rv/5Lar6HhobQ3Nzs02jf3agTERH5i6uOExER3cfmzJmDv/qrv8Izzzzjszr3rVu3Jvx+l8uFkZERZGZmAgAOHDggPffQQw/B7XZLK4tfvHgRjY2NE27n8ccfx549e6QGure3Fw0NDVPWq9Fo0N/fL339wQcf+OzjT3/6E0ZHR2E0GqfcltVqxW9/+1s0NDSgubkZzc3N6OzsREdHBz788MMpxxMREX1SbLSJiIjuc0ePHsWCBQvw2c9+Fvn5+Vi2bBkuXryI733vex/7Xo1Ggx/+8If4sz/7M3zmM5+RTs8GgKioKJw4cQJ//dd/jQULFuBXv/rVPRcm27dvH2JiYvDwww9j4cKF+PznP4/m5uYpa3366adx8uRJLF68GD/72c9w8+ZNPPXUU3jooYewePFiPPPMM/jVr36F5OTkKbf16quv4ktf+pLP0eqIiAhs2LABP//5z6ccT0RE9EkpvHffUJKIiIiIiIiIPjEe0SYiIiIiIiIKIDbaRERERERERAHERpuIiIiIiIgogNhoExEREREREQUQG20iIiIiIiKiAGKjTURERERERBRAbLSJiIiIiIiIAoiNNhEREREREVEA/T+TUl5dZSlkxwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1120x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw plot\n",
    "plt.figure(figsize=(14, 10), dpi=80)\n",
    "\n",
    "# Plotting the horizontal lines\n",
    "plt.hlines(\n",
    "    y=exp_agg.index,\n",
    "    linewidth=5,\n",
    "    xmin=0,\n",
    "    xmax=exp_agg.GradientSHAP,\n",
    "    colors=exp_agg.colors.values,\n",
    "    alpha=0.5,\n",
    ")\n",
    "# Decorations\n",
    "# Setting the labels of x-axis and y-axis\n",
    "plt.gca().set(ylabel=\"Features\", xlabel=\"GradientSHAP\")\n",
    "\n",
    "# Setting Date to y-axis\n",
    "plt.yticks(exp_agg.index, exp_agg.Features, fontsize=12)\n",
    "\n",
    "# Title of Bar Chart\n",
    "plt.title(\"GradientSHAP Global Explanation\", fontdict={\"size\": 20})\n",
    "\n",
    "# Optional grid layout\n",
    "plt.grid(linestyle=\"--\", alpha=0.5)\n",
    "\n",
    "# Displaying the Diverging Bar Chart\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  },
  "vscode": {
   "interpreter": {
    "hash": "c1644b008eb6c88a0ca3600445c5d81cce1a68be89616a3704b32e9da15a977e"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
